{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":657,"total_is_capped":false,"direct_labels_cover":3,"predictions_cover":657,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"97fef8ad440a","filters":{"topic":"Medical Imaging and Analysis"}},"results":[{"id":"W1964666848","doi":"10.1109/tbme.2014.2322864","title":"PLUS: Open-Source Toolkit for Ultrasound-Guided Intervention Systems","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":399,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Software; 3D ultrasound; Visualization; Rapid prototyping; Artificial intelligence; Medical physics; Computer vision; Real-time computing; Ultrasound; Engineering; Medicine; Radiology; Operating system","authors":[{"name":"András Lassó","is_ca":true},{"name":"Tamas Heffter","is_ca":true},{"name":"Adam Rankin","is_ca":true},{"name":"Csaba Pintér","is_ca":true},{"name":"Tamás Ungi","is_ca":true},{"name":"Gábor Fichtinger","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01417371626657189,"gpt":0.2469197383852262,"spread":0.2327460221186544,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001643276,0.002350077,0.001255089,0.001594737,0.0005283847,0.002008991,0.005113237,0.001696308,0.05283071],"category_scores_gemma":[0.007476712,0.001854327,0.001919283,0.00100623,0.0008203795,0.002857479,0.005091708,0.002355601,0.04115529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005645587,"about_ca_system_score_gemma":0.002057438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002127533,"about_ca_topic_score_gemma":0.002160587,"domain_scores_codex":[0.9985394,0.0002127971,0.0002188741,0.0002049409,0.0006706789,0.0001532599],"domain_scores_gemma":[0.9974751,0.001071037,0.0001779925,0.0003892903,0.0006568828,0.0002297598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002254725,0.000317671,0.001835953,0.004564578,0.0004305242,0.001422345,0.0009497661,0.03446769,0.04340896,0.03219396,0.5052695,0.3728844],"study_design_scores_gemma":[0.0005671031,0.0002959997,0.00231648,0.0005139783,0.0001492206,0.002178197,0.0001272103,0.141263,0.05186704,0.03296387,0.767299,0.0004589465],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001597925,0.0003852409,0.579926,0.0001576116,0.0001166011,0.0003712582,0.007424144,0.4037665,0.006254729],"genre_scores_gemma":[0.04866061,0.00175089,0.6349807,0.0008836619,0.0001741465,0.002905346,0.05754582,0.2137498,0.03934898],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.05283071,"threshold_uncertainty_score":0.1767364,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2589644515","doi":"10.1016/j.media.2017.11.005","title":"Learning normalized inputs for iterative estimation in medical image segmentation","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":229,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Computer science; Pipeline (software); Segmentation; Convolutional neural network; Artificial intelligence; Benchmark (surveying); Deep learning; Residual; Image segmentation; Pattern recognition (psychology); Computer vision; Algorithm","authors":[{"name":"Michal Drozdzal","is_ca":true},{"name":"Gabriel Chartrand","is_ca":false},{"name":"Eugene Vorontsov","is_ca":true},{"name":"Mahsa Shakeri","is_ca":true},{"name":"Lisa Di Jorio","is_ca":false},{"name":"An Tang","is_ca":true},{"name":"Adriana Romero","is_ca":false},{"name":"Yoshua Bengio","is_ca":false},{"name":"Chris Pal","is_ca":true},{"name":"Samuel Kadoury","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00796106501721744,"gpt":0.3132501830028003,"spread":0.3052891179855828,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002560676,0.001312573,0.001552477,0.001223924,0.0006072475,0.001771031,0.001866119,0.002761863,0.003038404],"category_scores_gemma":[0.01384029,0.001308623,0.001068897,0.001200796,0.001287171,0.002017539,0.002286552,0.002206892,0.001049339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001412441,"about_ca_system_score_gemma":0.002125641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007797223,"about_ca_topic_score_gemma":0.009101949,"domain_scores_codex":[0.9986512,0.0004202642,0.0001119091,0.0003353139,0.000354717,0.0001265377],"domain_scores_gemma":[0.9960176,0.002612331,0.000218475,0.0003024155,0.0007564085,0.00009273108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004034294,0.0001153897,0.001053613,0.0002852344,0.0001056251,0.0001045296,0.0002096186,0.4605519,0.02005388,0.01263675,0.002651157,0.5018288],"study_design_scores_gemma":[0.000006398981,0.00002207134,0.000125116,0.00001460428,0.000008087565,0.00002092117,0.000008805701,0.9908381,0.004788949,0.003686674,0.0004734519,0.000006825602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004370885,0.0001730177,0.9945012,0.00006536668,0.0000157629,0.0000244678,0.00002827594,0.0006038956,0.0002170871],"genre_scores_gemma":[0.1948673,0.0003634583,0.8011144,0.0001461116,0.0000542778,0.0002044878,0.000400207,0.0004642365,0.002385454],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007797223,"threshold_uncertainty_score":0.01550364,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2888443510","doi":"10.1016/j.media.2018.08.005","title":"Spine-GAN: Semantic segmentation of multiple spinal structures","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":215,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Concatenation (mathematics); Computer vision; Mathematics","authors":[{"name":"Zhongyi Han","is_ca":true},{"name":"Benzheng Wei","is_ca":false},{"name":"Ashley Mercado","is_ca":true},{"name":"Stephanie Leung","is_ca":true},{"name":"Shuo Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.007484894842775308,"gpt":0.27293141559568,"spread":0.2654465207529047,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007670088,0.001912544,0.001276745,0.001717981,0.0004890024,0.001902391,0.002221073,0.002431416,0.009103467],"category_scores_gemma":[0.001671459,0.001413352,0.002125577,0.001637437,0.0004822182,0.001021985,0.001704403,0.001994308,0.003774994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008684869,"about_ca_system_score_gemma":0.002229816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008595061,"about_ca_topic_score_gemma":0.02695572,"domain_scores_codex":[0.9995446,0.00007174553,0.00002624512,0.0001607379,0.000147578,0.00004902307],"domain_scores_gemma":[0.9996834,0.00009175752,0.00002502678,0.00009519895,0.00007925911,0.00002542347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007956666,0.0003307778,0.002074577,0.0009692371,0.0007563746,0.0003529479,0.0002257246,0.1552134,0.03819834,0.014111,0.1066006,0.6803712],"study_design_scores_gemma":[0.00009294375,0.00008237227,0.0009373617,0.00006443418,0.00008770664,0.000522457,0.00004661576,0.9499863,0.01590358,0.01401262,0.01821621,0.00004729217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0099249,0.0008397795,0.9487745,0.0003695731,0.0001822365,0.0002494051,0.004090023,0.03172008,0.003849536],"genre_scores_gemma":[0.1114076,0.0006781293,0.865662,0.0006213039,0.0001391343,0.0003446721,0.01033325,0.005864619,0.004949212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009103467,"threshold_uncertainty_score":0.03045416,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2586839650","doi":"10.1007/s00586-017-4956-3","title":"ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist","year":2017,"lang":"en","type":"article","venue":"European Spine Journal","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":210,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Engineering and Physical Sciences Research Council; Research Councils UK","keywords":"Medicine; Grading (engineering); Magnetic resonance imaging; Radiology; Radiological weapon; Neuroradiology; Lumbar; Discitis; Intervertebral disc; Neurology","authors":[{"name":"Amir Jamaludin","is_ca":false},{"name":"Meelis Lootus","is_ca":false},{"name":"Timor Kadir","is_ca":false},{"name":"Andrew Zisserman","is_ca":false},{"name":"Jill Urban","is_ca":false},{"name":"Michele C. Battié","is_ca":true},{"name":"Jeremy Fairbank","is_ca":false},{"name":"Iain W. McCall","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01958555070652137,"gpt":0.2884183246079902,"spread":0.2688327739014689,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02329641,0.001436777,0.001285943,0.001301888,0.0008912478,0.004495966,0.001589519,0.002926665,0.03938273],"category_scores_gemma":[0.02329844,0.0003271773,0.001041113,0.0008300545,0.00177063,0.002578367,0.002189326,0.002615277,0.01061329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002598334,"about_ca_system_score_gemma":0.005686875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001306464,"about_ca_topic_score_gemma":0.00121891,"domain_scores_codex":[0.9940163,0.001400295,0.0005530837,0.0005691109,0.002965398,0.0004958195],"domain_scores_gemma":[0.9792545,0.007569949,0.001365383,0.00112964,0.007269109,0.003411469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006087428,0.001806369,0.01612692,0.001186842,0.0003142917,0.0003304717,0.0003029102,0.006044086,0.01375249,0.01305529,0.1345581,0.8064348],"study_design_scores_gemma":[0.00218409,0.02712402,0.1124539,0.001943992,0.0008096907,0.002351012,0.00110558,0.04410847,0.04654865,0.06351145,0.697498,0.0003611406],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3051851,0.04989749,0.1496476,0.1940375,0.09674706,0.00315409,0.004938663,0.002730452,0.193662],"genre_scores_gemma":[0.667549,0.03947113,0.06403308,0.01278274,0.02233022,0.002134778,0.005511863,0.001417087,0.1847701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03938273,"threshold_uncertainty_score":0.1317483,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2161739205","doi":"10.1016/s0895-6111(03)00019-3","title":"3D/2D registration and segmentation of scoliotic vertebrae using statistical models","year":2003,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":173,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Centre Hospitalier de l’Université de Montréal; École de Technologie Supérieure; Université de Montréal; Centre Hospitalier Universitaire de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vertebra; Artificial intelligence; Computer science; Radiography; Computer vision; Segmentation; Image registration; Pattern recognition (psychology); Anatomy; Medicine; Image (mathematics); Radiology","authors":[{"name":"Max Mignotte","is_ca":true},{"name":"Stefan Parent","is_ca":true},{"name":"Hubert Labelle","is_ca":true},{"name":"Wafa Skalli","is_ca":false},{"name":"Jacques A. de Guise","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01572344107674521,"gpt":0.2558153045065191,"spread":0.2400918634297739,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001557939,0.0006498205,0.001026525,0.003750126,0.000556975,0.001944202,0.0008677842,0.001129971,0.001792297],"category_scores_gemma":[0.003335968,0.001006559,0.001749776,0.002667225,0.0007293368,0.0008960992,0.001119941,0.0009580206,0.001072631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006526962,"about_ca_system_score_gemma":0.00222154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007210235,"about_ca_topic_score_gemma":0.01177172,"domain_scores_codex":[0.9989777,0.0001965776,0.0001027957,0.0001911896,0.0004369922,0.00009476086],"domain_scores_gemma":[0.998971,0.0003946822,0.0001396329,0.0002061028,0.0002467906,0.00004175401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006187366,0.0001627981,0.009119691,0.000344102,0.000249146,0.0003426977,0.000407944,0.2581348,0.09321257,0.009000259,0.004040876,0.6243663],"study_design_scores_gemma":[0.00002950218,0.00008788893,0.006258012,0.00002060902,0.00007271054,0.0004331331,0.00006218941,0.9535517,0.03092087,0.005115952,0.00338957,0.00005785772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02970903,0.0002740464,0.9667299,0.0001170851,0.00003922799,0.00007256024,0.0002077254,0.002219832,0.0006306371],"genre_scores_gemma":[0.3296169,0.0003830189,0.665976,0.00008575171,0.00005778373,0.0002100976,0.0009995275,0.0008750486,0.001795756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007210235,"threshold_uncertainty_score":0.01433653,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2519302429","doi":"10.1007/s11914-016-0319-y","title":"Cortical Bone Porosity: What Is It, Why Is It Important, and How Can We Detect It?","year":2016,"lang":"en","type":"review","venue":"Current Osteoporosis Reports","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":164,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Canadian Institutes of Health Research","keywords":"Orthopedic surgery; Porosity; Cortical bone; Medicine; Geology; Anatomy; Surgery; Geotechnical engineering","authors":[{"name":"David M. L. Cooper","is_ca":true},{"name":"Chantal E. Kawalilak","is_ca":true},{"name":"Kimberly D. Harrison","is_ca":true},{"name":"Bryan D Johnston","is_ca":true},{"name":"James D. Johnston","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04284742007627886,"gpt":0.3130568566874509,"spread":0.2702094366111721,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001627509,0.000878167,0.002617641,0.003779912,0.0003821252,0.002062062,0.001303791,0.00205369,0.001690632],"category_scores_gemma":[0.004430798,0.0005444548,0.0007682292,0.003082124,0.00237741,0.002533261,0.0007372965,0.001990594,0.000809164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008292233,"about_ca_system_score_gemma":0.002694038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004125506,"about_ca_topic_score_gemma":0.005880832,"domain_scores_codex":[0.9992666,0.00009618433,0.0001401051,0.0001348068,0.0003176662,0.00004463101],"domain_scores_gemma":[0.9968899,0.001843148,0.0004847616,0.00005723542,0.0006337302,0.00009118046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001259639,0.00005836794,0.002660661,0.02232375,0.0002369244,0.0002785107,0.0001358715,0.000263531,0.0009016173,0.004309306,0.01498713,0.9537184],"study_design_scores_gemma":[0.00008781195,0.0002717473,0.01980487,0.03504236,0.001668405,0.01121863,0.001238486,0.0007682068,0.002999738,0.02357301,0.9031043,0.0002225136],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002482073,0.9977857,0.000222537,0.001233458,0.0001456918,0.000003148728,0.00002682785,0.000006255852,0.0003281574],"genre_scores_gemma":[0.003011255,0.9955569,0.0004063667,0.0003856753,0.0004215181,0.00000520633,0.00002706925,0.000002848641,0.0001831367],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004125506,"threshold_uncertainty_score":0.008607149,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2063213455","doi":"10.1016/j.neuroimage.2014.04.051","title":"Robust, accurate and fast automatic segmentation of the spinal cord","year":2014,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":156,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; MetLife Foundation; Fonds de Recherche du Québec - Santé; National Multiple Sclerosis Society","keywords":"Segmentation; Spinal cord; Artificial intelligence; Initialization; Computer science; Computer vision; Robustness (evolution); Orientation (vector space); Pattern recognition (psychology); Mathematics; Medicine; Geometry","authors":[{"name":"Benjamin De Leener","is_ca":true},{"name":"Samuel Kadoury","is_ca":true},{"name":"Julien Cohen‐Adad","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02150977349920347,"gpt":0.2470549803939322,"spread":0.2255452068947287,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009674997,0.001128174,0.00115985,0.002405465,0.0004633483,0.002177736,0.001298327,0.002099253,0.002216007],"category_scores_gemma":[0.003293066,0.001004447,0.001073872,0.001240308,0.0005746895,0.001056668,0.001345505,0.001360129,0.001808102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006610699,"about_ca_system_score_gemma":0.002254762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006349159,"about_ca_topic_score_gemma":0.01050545,"domain_scores_codex":[0.9990038,0.0001156775,0.00006253334,0.0001842088,0.0005246484,0.0001092087],"domain_scores_gemma":[0.9987621,0.000424093,0.0001573599,0.0002410008,0.0003597819,0.00005558958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006450147,0.0001077501,0.001918489,0.0004521488,0.0002886264,0.0003546852,0.0001464943,0.08105518,0.2613927,0.003378546,0.01051144,0.639749],"study_design_scores_gemma":[0.00006610816,0.0001555072,0.007782514,0.00004469234,0.0001598319,0.001614144,0.00006715446,0.8318189,0.1440722,0.005765534,0.008355094,0.0000982531],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03054482,0.00226081,0.9607136,0.0004019147,0.0001441271,0.00007827665,0.0005016401,0.003975598,0.001379257],"genre_scores_gemma":[0.2573659,0.001394143,0.7302397,0.0002816153,0.0002513328,0.0001154068,0.00131288,0.001231724,0.007807364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006349159,"threshold_uncertainty_score":0.01262438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2061075439","doi":"10.1007/bf02344767","title":"3D reconstruction method from biplanar radiography using non-stereocorresponding points and elastic deformable meshes","year":2000,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":152,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine; Hôpital Notre-Dame; École de Technologie Supérieure","funders":"","keywords":"Projection (relational algebra); 3D reconstruction; Point (geometry); Polygon mesh; Geometry; Line (geometry); Position (finance); Radiography; Deformation (meteorology); Computer vision; Cadaveric spasm; Artificial intelligence; Computer science; Mathematics; Anatomy; Algorithm; Physics","authors":[{"name":"David Mitton","is_ca":false},{"name":"C. Landry","is_ca":true},{"name":"Santiago R. Verón","is_ca":true},{"name":"Wafa Skalli","is_ca":false},{"name":"F. Lavaste","is_ca":false},{"name":"Jacques A. de Guise","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00956152793717083,"gpt":0.2292201804463542,"spread":0.2196586525091834,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006103927,0.0008304327,0.0009334507,0.001830897,0.0003610269,0.001394135,0.001392267,0.001502373,0.004720994],"category_scores_gemma":[0.001440068,0.001218313,0.001561449,0.001083304,0.0003750179,0.0007636143,0.001062037,0.001216516,0.001641583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004494412,"about_ca_system_score_gemma":0.001085214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002504616,"about_ca_topic_score_gemma":0.002531934,"domain_scores_codex":[0.9994206,0.00006589646,0.00003465479,0.00008227243,0.0003653539,0.00003116512],"domain_scores_gemma":[0.999488,0.0001603599,0.00004329514,0.0001142467,0.0001629207,0.00003122607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003224191,0.0001831912,0.001310261,0.0004252192,0.0001905558,0.0005571275,0.0002527313,0.2382561,0.1303044,0.01253854,0.003731579,0.6119279],"study_design_scores_gemma":[0.00002789124,0.00005596534,0.0006650293,0.00001971943,0.00004428105,0.0006229799,0.00003248054,0.9639717,0.02833254,0.002852572,0.003328657,0.00004616764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004490604,0.00005177943,0.9939553,0.00003703644,0.00002212114,0.00003431736,0.00005826306,0.0007699417,0.0005806276],"genre_scores_gemma":[0.07595783,0.0002344302,0.9208153,0.00005041793,0.00002288056,0.00006841364,0.0003288822,0.0003711419,0.002150777],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004720994,"threshold_uncertainty_score":0.01579326,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2067484188","doi":"10.1109/tmi.2013.2268424","title":"Lumbar Spine Segmentation Using a Statistical Multi-Vertebrae Anatomical Shape+Pose Model","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":145,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Artificial intelligence; Segmentation; Computer science; Computer vision; Image segmentation; Preprocessor; Robustness (evolution); Pattern recognition (psychology); Statistical model; Vertebra; Lumbar vertebrae; Active shape model; Lumbar; Medicine; Radiology; Anatomy","authors":[{"name":"Abtin Rasoulian","is_ca":true},{"name":"Robert Rohling","is_ca":true},{"name":"Purang Abolmaesumi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01596352744376946,"gpt":0.2765623096275439,"spread":0.2605987821837744,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007193725,0.0004894821,0.0007233294,0.0009579381,0.0002579639,0.0008462246,0.001150682,0.001322141,0.0008501516],"category_scores_gemma":[0.001753017,0.000693429,0.001669704,0.0009142198,0.000653457,0.0008640601,0.0007703053,0.0007576055,0.0006339049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005447519,"about_ca_system_score_gemma":0.0009984976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003594183,"about_ca_topic_score_gemma":0.004780707,"domain_scores_codex":[0.9993365,0.0001458534,0.00004108902,0.0001598603,0.000276357,0.00004018614],"domain_scores_gemma":[0.9995099,0.0001883263,0.0000951406,0.00009616381,0.00008546154,0.00002503595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000762042,0.00004880232,0.001768874,0.00004443125,0.00007454635,0.0001113027,0.00006146487,0.9103739,0.01610266,0.003468847,0.0005571428,0.06731178],"study_design_scores_gemma":[0.000004058377,0.00002756469,0.0006728249,0.00000278347,0.00001025415,0.00008697075,0.000004122594,0.9964309,0.001109365,0.001263733,0.0003781561,0.000009325862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01168326,0.0001382539,0.9871567,0.0001028627,0.00001766052,0.00003005006,0.00006889735,0.0003809515,0.0004212964],"genre_scores_gemma":[0.6100017,0.0007312759,0.3841013,0.0002794108,0.0001095454,0.0003497955,0.0007600529,0.0003369766,0.003329978],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003594183,"threshold_uncertainty_score":0.007146537,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2222318341","doi":"10.1016/j.compmedimag.2015.12.006","title":"A multi-center milestone study of clinical vertebral CT segmentation","year":2016,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":137,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; University of British Columbia","funders":"National Institutes of Health","keywords":"Segmentation; Vertebra; Sørensen–Dice coefficient; Milestone; Medicine; Thoracic vertebrae; Computer science; Artificial intelligence; Image segmentation; Lumbar; Lumbar vertebrae; Radiology; Anatomy; Cartography","authors":[{"name":"Jianhua Yao","is_ca":false},{"name":"Joseph E. Burns","is_ca":false},{"name":"Daniel Forsberg","is_ca":false},{"name":"Alexander Seitel","is_ca":true},{"name":"Abtin Rasoulian","is_ca":true},{"name":"Purang Abolmaesumi","is_ca":true},{"name":"Kerstin Hammernik","is_ca":false},{"name":"Martin Urschler","is_ca":false},{"name":"Bulat Ibragimov","is_ca":false},{"name":"Robert Korez","is_ca":false},{"name":"Tomaž Vrtovec","is_ca":false},{"name":"Isaac Castro-Mateos","is_ca":false},{"name":"José M. Pozo","is_ca":false},{"name":"Alejandro F. Frangi","is_ca":false},{"name":"Ronald M. Summers","is_ca":false},{"name":"Shuo Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02823088218053465,"gpt":0.3265334777514597,"spread":0.298302595570925,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00119043,0.0003157068,0.0004079817,0.002608988,0.0005101023,0.0008362633,0.0006482086,0.0009524091,0.001790861],"category_scores_gemma":[0.004845984,0.0004075413,0.0003345406,0.001293426,0.0003928577,0.0008226893,0.000767148,0.0004198408,0.0006481477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007377614,"about_ca_system_score_gemma":0.0004496904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005464217,"about_ca_topic_score_gemma":0.007611659,"domain_scores_codex":[0.9994119,0.0001365784,0.00006444778,0.0001806893,0.0001166377,0.00008973067],"domain_scores_gemma":[0.9965719,0.001127882,0.0003045257,0.0005022304,0.001181139,0.0003124358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007899567,0.002601647,0.7532223,0.0002782308,0.0004411486,0.00842508,0.002252828,0.01008029,0.07307161,0.0007811508,0.002132396,0.1388137],"study_design_scores_gemma":[0.0001198187,0.001698441,0.9387655,0.00003292719,0.000207675,0.01416651,0.001454092,0.02310156,0.01776535,0.0003203391,0.002264775,0.0001028934],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966336,0.0001478739,0.002043134,0.00003709309,0.000004807467,0.00004273096,0.0003964181,0.00004459635,0.0006497921],"genre_scores_gemma":[0.9974774,0.00006495029,0.001496095,0.0000156757,0.000007647043,0.00001217676,0.0005445503,0.00004812677,0.0003335306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005464217,"threshold_uncertainty_score":0.01086485,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2100843879","doi":"10.1016/s8756-3282(01)00425-2","title":"Inhomogeneity of human vertebral cancellous bone: systematic density and structure patterns inside the vertebral body","year":2001,"lang":"en","type":"article","venue":"Bone","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":127,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Mount Sinai Hospital","funders":"Fonds De La Recherche Scientifique - FNRS","keywords":"Cancellous bone; Sagittal plane; Coronal plane; Transverse plane; Anatomy; Vertebra; Materials science; Quantitative computed tomography; Lumbar vertebrae; Bone density; Vertebral body; Lumbar; Biomedical engineering; Nuclear medicine; Medicine; Osteoporosis","authors":[{"name":"Xavier Banse","is_ca":false},{"name":"Jean‐Pierre Devogelaer","is_ca":false},{"name":"Everard Munting","is_ca":false},{"name":"Christian Delloye","is_ca":false},{"name":"Olivier Cornu","is_ca":false},{"name":"Marc D. Grynpas","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.007044593639610698,"gpt":0.211332641878806,"spread":0.2042880482391953,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001375042,0.00006798023,0.0001102735,0.0005390481,0.0001473216,0.0002649887,0.0001485842,0.0002246022,0.001409748],"category_scores_gemma":[0.0007059305,0.0001883027,0.00008292647,0.0003140578,0.0003627021,0.0001689563,0.0001586592,0.0001061642,0.0002276933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001279118,"about_ca_system_score_gemma":0.0002728941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00429901,"about_ca_topic_score_gemma":0.00623795,"domain_scores_codex":[0.9999624,0.00000837468,0.000001430574,0.000006863185,0.00001404263,0.000006843133],"domain_scores_gemma":[0.9997602,0.0001392693,0.00002396174,0.00002154131,0.00003402213,0.00002106129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001534621,0.00008125341,0.06825351,0.0002522885,0.00009521462,0.000810224,0.001016857,0.006832478,0.8719199,0.0005552501,0.0005409097,0.04810753],"study_design_scores_gemma":[0.00007226743,0.0002773646,0.8976897,0.00002539532,0.0001580177,0.005949554,0.000652216,0.01624984,0.07545144,0.001175552,0.002255424,0.00004325991],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894376,0.0006488134,0.008492099,0.00004964464,0.000003418558,0.00001355225,0.0001358573,0.00004172404,0.001177414],"genre_scores_gemma":[0.998018,0.0001935046,0.0009167563,0.00002233227,0.000004531606,0.000006362749,0.00008450276,0.00002324345,0.0007307088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00429901,"threshold_uncertainty_score":0.008547962,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2959828872","doi":"10.1016/j.media.2019.07.005","title":"Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Canadian Institutes of Health Research; Prostate Cancer Canada","keywords":"Artificial intelligence; Segmentation; Computer science; Convolutional neural network; Hausdorff distance; Deep learning; Pattern recognition (psychology); Image segmentation; Computer vision","authors":[{"name":"Davood Karimi","is_ca":true},{"name":"Qi Zeng","is_ca":true},{"name":"Prateek Mathur","is_ca":true},{"name":"Apeksha Avinash","is_ca":true},{"name":"S. Sara Mahdavi","is_ca":false},{"name":"Ingrid Spadinger","is_ca":false},{"name":"Purang Abolmaesumi","is_ca":true},{"name":"Septimiu E. Salcudean","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.006231307933401923,"gpt":0.2571856918820442,"spread":0.2509543839486423,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006886386,0.0008004413,0.0008085419,0.001325715,0.0003104722,0.001277541,0.0009359858,0.001273408,0.001079573],"category_scores_gemma":[0.002087685,0.0007076134,0.0007816554,0.0007444024,0.0003942872,0.0006870224,0.001054305,0.001170603,0.0009431109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008779265,"about_ca_system_score_gemma":0.001624389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008458531,"about_ca_topic_score_gemma":0.0133853,"domain_scores_codex":[0.9995751,0.00007580217,0.00002668355,0.0001036169,0.0001540419,0.00006470281],"domain_scores_gemma":[0.9994498,0.0001973304,0.00008434674,0.00007902166,0.0001511542,0.0000383284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005576046,0.0001730439,0.004704155,0.0003337968,0.0001558607,0.0002200462,0.0001817361,0.2910927,0.09065736,0.003765982,0.009652468,0.5985053],"study_design_scores_gemma":[0.00000714125,0.00002503087,0.001571216,0.00001984154,0.00001705263,0.0001479953,0.00001635883,0.9810334,0.01429058,0.001725957,0.001131176,0.00001416202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06617511,0.001475948,0.9257796,0.0005207102,0.00007428793,0.00007768265,0.0005586451,0.003704649,0.001633408],"genre_scores_gemma":[0.6291354,0.001102756,0.359718,0.0004661902,0.0001289048,0.0001096229,0.001734022,0.0009068413,0.006698213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008458531,"threshold_uncertainty_score":0.01681858,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2170750180","doi":"10.1109/titb.2005.855526","title":"A Support Vector Machines Classifier to Assess the Severity of Idiopathic Scoliosis From Surface Topography","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Glenrose Rehabilitation Hospital; University of Alberta","funders":"","keywords":"Support vector machine; Artificial intelligence; Scoliosis; Pattern recognition (psychology); Classifier (UML); Linear discriminant analysis; Trunk; Idiopathic scoliosis; Computer science; Curvature; Decision tree; Mathematics; Medicine; Surgery","authors":[{"name":"L. Ramirez","is_ca":true},{"name":"N.G. Durdle","is_ca":true},{"name":"V.J. Raso","is_ca":true},{"name":"Doug Hill","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00853636648952531,"gpt":0.2299838446984963,"spread":0.221447478208971,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001695104,0.0007169583,0.0009732437,0.001818766,0.0003242343,0.0007280608,0.0006590924,0.0008951579,0.001283966],"category_scores_gemma":[0.00474239,0.0001953831,0.0005537933,0.00107075,0.0001603529,0.0007029635,0.0003096047,0.0007140157,0.0009218231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003707586,"about_ca_system_score_gemma":0.0005904315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002572676,"about_ca_topic_score_gemma":0.001918504,"domain_scores_codex":[0.9990414,0.000193241,0.0001300799,0.0001685741,0.0003479197,0.0001187942],"domain_scores_gemma":[0.9976436,0.0009295056,0.0001297768,0.0001152913,0.0010915,0.00009029097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006644414,0.0005672922,0.02043649,0.0001725982,0.0001849569,0.0002836137,0.00008425863,0.04670684,0.02319195,0.0008520743,0.007960766,0.8988947],"study_design_scores_gemma":[0.00006375822,0.0004803293,0.01172504,0.00002827463,0.00005799741,0.0002410261,0.00006032766,0.9732935,0.01156776,0.000744212,0.001704237,0.00003368844],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3858409,0.001189792,0.6025438,0.0004725604,0.0004664668,0.000580026,0.001887275,0.003950979,0.003068129],"genre_scores_gemma":[0.8344807,0.0002215574,0.1605637,0.00008524369,0.00009312091,0.0003143591,0.001738792,0.00003671366,0.002465843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002572676,"threshold_uncertainty_score":0.008964717,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2604009228","doi":"10.1007/s11548-017-1575-8","title":"SLIDE: automatic spine level identification system using a deep convolutional neural network","year":2017,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"B.C. Women's Hospital & Health Centre; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Computer vision; Identification (biology); Medicine","authors":[{"name":"Jorden Hetherington","is_ca":true},{"name":"Victoria A. Lessoway","is_ca":true},{"name":"V. Gunka","is_ca":true},{"name":"Purang Abolmaesumi","is_ca":true},{"name":"Robert Rohling","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03669003851331305,"gpt":0.274414423715696,"spread":0.2377243852023829,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001753308,0.0008119611,0.0005484573,0.0008238417,0.0003225149,0.0004851459,0.0009268275,0.0008844104,0.01931679],"category_scores_gemma":[0.000394576,0.0004989157,0.0003595728,0.0002870267,0.0001040495,0.0005316371,0.000999937,0.0005164933,0.006403745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003671591,"about_ca_system_score_gemma":0.0007255573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004016703,"about_ca_topic_score_gemma":0.012441,"domain_scores_codex":[0.9998672,0.000006050078,0.000008610268,0.00004770906,0.00004924388,0.00002126275],"domain_scores_gemma":[0.9998574,0.00001773299,0.00001038289,0.00001833675,0.00006920566,0.00002681186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009472634,0.0001665687,0.00947685,0.0004108161,0.0001623079,0.0005973871,0.00006307153,0.006060113,0.1421791,0.0006039887,0.06186979,0.7774627],"study_design_scores_gemma":[0.000379754,0.001232522,0.06437,0.0002025071,0.0002810009,0.003718826,0.0001681201,0.6211843,0.2526645,0.003255609,0.05225014,0.0002926124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1788157,0.002290138,0.680474,0.001171072,0.001570929,0.001305055,0.01975134,0.09822539,0.01639634],"genre_scores_gemma":[0.5335309,0.0007892466,0.388044,0.001188087,0.0003035747,0.000679801,0.01882968,0.001517173,0.05511762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01931679,"threshold_uncertainty_score":0.06462109,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2321283863","doi":"10.1016/j.compmedimag.2016.02.002","title":"Multi-modal vertebrae recognition using Transformed Deep Convolution Network","year":2016,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Computer science; Vertebra; Feature (linguistics); Deep learning; Pattern recognition (psychology); Process (computing); Computer vision; Convolution (computer science); Convolutional neural network; Representation (politics); Modal; Artificial neural network; Anatomy; Medicine","authors":[{"name":"Yunliang Cai","is_ca":true},{"name":"Mark Landis","is_ca":true},{"name":"David Laidley","is_ca":true},{"name":"Anat Kornecki","is_ca":true},{"name":"Andrea Lum","is_ca":true},{"name":"Shuo Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0174755098400779,"gpt":0.2395417669989261,"spread":0.2220662571588482,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003289065,0.0006343147,0.0008223703,0.000953927,0.000289023,0.0008109099,0.001065277,0.001172714,0.002470737],"category_scores_gemma":[0.0006882377,0.0004794388,0.001129244,0.0009625739,0.0002743796,0.0007550868,0.001004421,0.0009251768,0.001217451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004694327,"about_ca_system_score_gemma":0.0008400425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007925732,"about_ca_topic_score_gemma":0.01153736,"domain_scores_codex":[0.9996865,0.00002505418,0.00001715047,0.0001208343,0.00009086895,0.00005956973],"domain_scores_gemma":[0.9997275,0.00005675236,0.00003814245,0.00005073963,0.0001000618,0.0000267007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003467041,0.0001604812,0.004507795,0.0001249195,0.0001890257,0.0002243028,0.00008914765,0.1022788,0.06161057,0.002820672,0.004180064,0.8234675],"study_design_scores_gemma":[0.000005139849,0.0000306432,0.001449919,0.000008802277,0.00002822647,0.0001168558,0.00001625273,0.9882376,0.007916756,0.00155384,0.0006238206,0.00001222171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0401204,0.0004196782,0.9552966,0.0001837144,0.00008079585,0.00004227427,0.0003318112,0.001950696,0.001573946],"genre_scores_gemma":[0.6525236,0.0006220843,0.3376552,0.0002604936,0.00009882444,0.0001095763,0.001319678,0.0001902329,0.007220497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007925732,"threshold_uncertainty_score":0.01575917,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3048848678","doi":"10.1016/j.wneu.2020.04.022","title":"Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care","year":2020,"lang":"en","type":"review","venue":"World Neurosurgery","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":90,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Western Hospital; University Health Network; University of Toronto","funders":"","keywords":"Medicine; Artificial intelligence; Machine learning; Personalized medicine; Precision medicine; Health care; Deep learning; Disease; Data science; Computer science; Bioinformatics; Pathology","authors":[{"name":"Omar Khan","is_ca":true},{"name":"Jetan H. Badhiwala","is_ca":true},{"name":"Giovanni Grasso","is_ca":false},{"name":"Michael G. Fehlings","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1584254649450629,"gpt":0.3198380437143746,"spread":0.1614125787693116,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009378251,0.00075056,0.001112239,0.002229009,0.0002092295,0.001337661,0.0009878461,0.001110268,0.00327131],"category_scores_gemma":[0.001943131,0.0002024162,0.0005603135,0.001863032,0.0005504183,0.00147858,0.0007768543,0.001943535,0.001236618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007454754,"about_ca_system_score_gemma":0.001354102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001828669,"about_ca_topic_score_gemma":0.002910059,"domain_scores_codex":[0.9996954,0.0000574671,0.00003440128,0.00005622803,0.0001388874,0.00001758724],"domain_scores_gemma":[0.9988536,0.0007307412,0.00009028811,0.00003230227,0.0002498366,0.00004329841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000297296,0.00007821181,0.000279465,0.009666352,0.000113361,0.00008427529,0.00003473665,0.0008795578,0.000935495,0.01118058,0.01136941,0.9653488],"study_design_scores_gemma":[0.00002375848,0.0001610055,0.002053176,0.008590735,0.0002888061,0.00105647,0.0001076981,0.001528541,0.001190237,0.01670238,0.9682416,0.00005559464],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002320737,0.995194,0.00133676,0.0007325677,0.0002779493,0.00001027781,0.00003097047,0.00001868575,0.002166679],"genre_scores_gemma":[0.002109938,0.9945362,0.001750887,0.0004847205,0.0003581383,0.00001078377,0.00005548332,0.000004302412,0.0006895174],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00327131,"threshold_uncertainty_score":0.01094365,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2102955128","doi":"10.1016/s0268-0033(02)00032-3","title":"Relationship between pain and vertebral motion in chronic low-back pain subjects","year":2002,"lang":"en","type":"article","venue":"Clinical Biomechanics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University; University of Guelph","funders":"North American Spine Society","keywords":"Medicine; Low back pain; Lumbar; Back pain; Physical medicine and rehabilitation; Chronic pain; Referred pain; Physical therapy; Surgery","authors":[{"name":"James P. Dickey","is_ca":true},{"name":"M.R. Pierrynowski","is_ca":true},{"name":"Drew A. Bednar","is_ca":true},{"name":"Simon X. Yang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06003873225448678,"gpt":0.2914187784093409,"spread":0.2313800461548542,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002546845,0.0002096999,0.0002462183,0.0008043802,0.0002976456,0.0003853427,0.0001646657,0.0005764104,0.003106704],"category_scores_gemma":[0.002639927,0.0001589433,0.0001691862,0.00045561,0.0002211216,0.0002467449,0.0002338864,0.0002947477,0.0003407104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001362256,"about_ca_system_score_gemma":0.0001690982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003122897,"about_ca_topic_score_gemma":0.004854931,"domain_scores_codex":[0.99986,0.00003898718,0.00001830749,0.00002069523,0.00002308022,0.00003882],"domain_scores_gemma":[0.9985381,0.0007337928,0.0003341586,0.00003203527,0.0001335785,0.0002284083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001429952,0.0002051217,0.9894855,0.00003244631,0.00006964808,0.0003799145,0.0001844501,0.0001153142,0.004511128,0.00002954045,0.00007479576,0.003482178],"study_design_scores_gemma":[0.000009082818,0.0002857115,0.9991003,0.000001847243,0.00001442072,0.0002494976,0.00009830647,0.000127728,0.00006312715,0.00001205748,0.00003625153,0.000001792476],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999369,0.0001517525,0.00004384127,0.00002418948,0.000003202603,0.000003541377,0.00008809799,0.000001707954,0.0003147172],"genre_scores_gemma":[0.9995856,0.00005385092,0.00003385502,0.00001159523,0.00001104445,0.000004068035,0.0001046938,7.784206e-7,0.0001944357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003122897,"threshold_uncertainty_score":0.01039296,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2149402078","doi":"10.1109/tmi.2013.2244903","title":"Spine Segmentation in Medical Images Using Manifold Embeddings and Higher-Order MRFs","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Segmentation; Nonlinear dimensionality reduction; Markov random field; Manifold (fluid mechanics); Image segmentation; Computer science; Active shape model; Mathematics; Computer vision; Dimensionality reduction","authors":[{"name":"Samuel Kadoury","is_ca":true},{"name":"Hubert Labelle","is_ca":true},{"name":"Nikos Paragios","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008213767717886327,"gpt":0.2615627631980623,"spread":0.253348995480176,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001008429,0.000817452,0.0009202685,0.001676144,0.000375873,0.001094063,0.001226375,0.001398767,0.00103122],"category_scores_gemma":[0.003970651,0.000641295,0.001624979,0.0009977852,0.001026094,0.001758076,0.001309949,0.001296729,0.000551971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009278844,"about_ca_system_score_gemma":0.0007936482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003972874,"about_ca_topic_score_gemma":0.004152275,"domain_scores_codex":[0.9994407,0.0001893569,0.00003190713,0.0001382819,0.0001489682,0.00005071734],"domain_scores_gemma":[0.9989911,0.0004679777,0.0002096701,0.0001605181,0.0001240402,0.00004661369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007799626,0.0000432786,0.001101973,0.0001056125,0.00005923025,0.0001338535,0.0001614553,0.8409091,0.01096224,0.02545797,0.0007616998,0.1202256],"study_design_scores_gemma":[0.000001824836,0.0000165493,0.0001027586,0.000004043435,0.000002378564,0.0000308481,0.000005751474,0.9915549,0.0007027851,0.007294892,0.0002778241,0.0000053772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009006409,0.0000885506,0.9902872,0.00008605313,0.000006677839,0.00001630949,0.00002924676,0.0002799253,0.0001997038],"genre_scores_gemma":[0.4123599,0.0004436831,0.5845591,0.00012068,0.00008645948,0.0001353333,0.0004404926,0.0003011232,0.00155317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003972874,"threshold_uncertainty_score":0.007899523,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2001770256","doi":"10.1007/s10278-008-9127-y","title":"Automatic Cobb Measurement of Scoliosis Based on Fuzzy Hough Transform with Vertebral Shape Prior","year":2008,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Glenrose Rehabilitation Hospital; University of Alberta","funders":"","keywords":"Scoliosis; Hough transform; CobB; Fuzzy logic; Computer science; Cobb angle; Artificial intelligence; Computer vision; Medicine; Image (mathematics); Surgery","authors":[{"name":"Junhua Zhang","is_ca":true},{"name":"Edmond Lou","is_ca":true},{"name":"Lawrence H. Le","is_ca":true},{"name":"Douglas L. Hill","is_ca":true},{"name":"James Raso","is_ca":true},{"name":"Yuanyuan Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01398228072220398,"gpt":0.2050139144636251,"spread":0.1910316337414211,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004155567,0.0003588225,0.0006968573,0.001460964,0.0003211459,0.0006507644,0.000539788,0.000800445,0.001039765],"category_scores_gemma":[0.001504878,0.0004073422,0.0003679612,0.0008559484,0.0003534711,0.0006756683,0.0004695815,0.000565023,0.0004355597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002595415,"about_ca_system_score_gemma":0.0008897914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004302733,"about_ca_topic_score_gemma":0.006374815,"domain_scores_codex":[0.999488,0.00006481222,0.00002178198,0.00008462811,0.0002916699,0.00004908083],"domain_scores_gemma":[0.9992605,0.0002173112,0.00005532773,0.00009707003,0.0003213425,0.00004849557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006207525,0.0001131258,0.006790811,0.0001780553,0.00006493576,0.0001247851,0.00014896,0.02393056,0.3489845,0.001885271,0.001608855,0.6155493],"study_design_scores_gemma":[0.00003712341,0.0001250907,0.02633279,0.00002864686,0.0000656767,0.0008842631,0.00007909402,0.8726545,0.09607379,0.001818701,0.001840276,0.00006007981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1042137,0.0003402352,0.8929744,0.00009663939,0.00003107365,0.00004877869,0.0000990909,0.0008066061,0.001389527],"genre_scores_gemma":[0.7026286,0.0002791789,0.2954239,0.00004914905,0.00003222683,0.00003713738,0.0002387344,0.000108933,0.001202104],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004302733,"threshold_uncertainty_score":0.008555412,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3162804940","doi":"10.1038/s41598-021-89848-3","title":"A deep learning model for detection of cervical spinal cord compression in MRI scans","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University Health Network; St. Michael's Hospital; University of Toronto","funders":"AOSpine","keywords":"Medicine; Magnetic resonance imaging; Deep learning; Spinal cord compression; Convolutional neural network; Spinal cord; Cord; Myelopathy; Radiology; Artificial intelligence; Computer science; Surgery","authors":[{"name":"Zamir Merali","is_ca":true},{"name":"Justin Z. Wang","is_ca":true},{"name":"Jetan H. Badhiwala","is_ca":true},{"name":"Christopher D. Witiw","is_ca":true},{"name":"Jefferson R. Wilson","is_ca":true},{"name":"Michael G. Fehlings","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01730322683408581,"gpt":0.2676090157718528,"spread":0.2503057889377669,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009050386,0.0007037575,0.0005725378,0.0006606467,0.0002379107,0.0005777469,0.0009477788,0.001062654,0.001040752],"category_scores_gemma":[0.002563667,0.0003190372,0.0006512195,0.0003815444,0.0002681976,0.0004785009,0.0005556539,0.0009980415,0.0003686016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001098216,"about_ca_system_score_gemma":0.001087813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02061663,"about_ca_topic_score_gemma":0.01661526,"domain_scores_codex":[0.9997292,0.00006281205,0.00002287253,0.00008064019,0.00005057314,0.00005385958],"domain_scores_gemma":[0.9993302,0.0003422245,0.0000649967,0.00004288508,0.0001857135,0.0000340417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007159693,0.0003462357,0.01881155,0.0001077515,0.000182397,0.0003705173,0.00007163705,0.7772994,0.008241224,0.001075328,0.005237851,0.1875402],"study_design_scores_gemma":[0.000007789704,0.00003373929,0.0009316105,0.000007726548,0.00001002497,0.00002236043,0.000004250807,0.9977509,0.0008042358,0.0003036742,0.0001190226,0.000004605038],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6451966,0.002538286,0.3407983,0.001956712,0.0002197411,0.0002197556,0.002208441,0.003431989,0.003430223],"genre_scores_gemma":[0.9589925,0.0003419593,0.03596288,0.0002862954,0.00004628622,0.0001272402,0.001676553,0.00003976966,0.002526482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02061663,"threshold_uncertainty_score":0.04099321,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2395197407","doi":"10.1007/978-3-319-24574-4_81","title":"Fast Automatic Vertebrae Detection and Localization in Pathological CT Scans - A Deep Learning Approach","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Vertebra; Deep learning; Field (mathematics); Pattern recognition (psychology); Medicine; Anatomy; Mathematics","authors":[{"name":"Amin Suzani","is_ca":true},{"name":"Alexander Seitel","is_ca":true},{"name":"Yuan Liu","is_ca":true},{"name":"Sidney Fels","is_ca":true},{"name":"Robert Rohling","is_ca":true},{"name":"Purang Abolmaesumi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.012901400595629,"gpt":0.2162345663665514,"spread":0.2033331657709224,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008639079,0.001214824,0.001228855,0.002224051,0.0003524129,0.001660685,0.00196255,0.002100949,0.004425896],"category_scores_gemma":[0.001849438,0.001135698,0.001367735,0.001478488,0.0003481358,0.001122578,0.001647437,0.001683314,0.003056092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005793607,"about_ca_system_score_gemma":0.001239746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00654658,"about_ca_topic_score_gemma":0.01331302,"domain_scores_codex":[0.9995234,0.0000468434,0.00003541312,0.0001243106,0.0001743263,0.00009580253],"domain_scores_gemma":[0.999215,0.0003088713,0.00007876612,0.0001184626,0.0002271522,0.00005178808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002707256,0.00008633794,0.002329472,0.0002630158,0.00009366587,0.0001911932,0.0000496079,0.03414685,0.03197121,0.001614469,0.007004444,0.9219791],"study_design_scores_gemma":[0.00002495943,0.00007292347,0.002969009,0.00006677687,0.0000731878,0.0008963425,0.00004601309,0.9674096,0.01972122,0.005182404,0.003500874,0.00003664875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01661579,0.001932664,0.9755681,0.0002928556,0.00009070133,0.00007773953,0.0006368865,0.00330587,0.001479447],"genre_scores_gemma":[0.2041368,0.002531227,0.7811577,0.0002651282,0.0001614112,0.0001241241,0.001785888,0.0005707314,0.009266948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00654658,"threshold_uncertainty_score":0.01480603,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2131549177","doi":"10.1109/tbme.2006.889205","title":"A Novel System for the 3-D Reconstruction of the Human Spine and Rib Cage From Biplanar X-Ray Images","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"McGill University; Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal; Advanced Micro Devices (Canada)","funders":"","keywords":"Epipolar geometry; Calibration; Computer vision; Artificial intelligence; 3D reconstruction; Iterative reconstruction; Computer science; Object (grammar); Motion (physics); Mathematics; Image (mathematics)","authors":[{"name":"Farida Chériet","is_ca":true},{"name":"Catherine Laporte","is_ca":true},{"name":"Samuel Kadoury","is_ca":true},{"name":"Hubert Labelle","is_ca":true},{"name":"J. Dansereau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00816985694559376,"gpt":0.2104636327103087,"spread":0.2022937757647149,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006267835,0.0003605892,0.000492345,0.0003843478,0.0002547357,0.0007425821,0.0009031456,0.0007089806,0.004080701],"category_scores_gemma":[0.001031769,0.0004291632,0.0003381383,0.0003729606,0.000341583,0.0007130229,0.0009602742,0.0006024403,0.001770149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003005201,"about_ca_system_score_gemma":0.000949203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001028053,"about_ca_topic_score_gemma":0.00194524,"domain_scores_codex":[0.9996707,0.00004823665,0.00002058126,0.00008713247,0.000152522,0.00002069816],"domain_scores_gemma":[0.9996412,0.00008531705,0.00003366436,0.0001004192,0.0001099423,0.00002944047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001887912,0.00007591801,0.001605555,0.0004237689,0.00007406077,0.0003741817,0.0002184339,0.0134558,0.5699428,0.006993995,0.003602632,0.403044],"study_design_scores_gemma":[0.0002189751,0.001116238,0.01248941,0.0001202852,0.0002279542,0.008877268,0.0001254969,0.4573739,0.4060659,0.002790367,0.1103387,0.0002553785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005561437,0.0001295733,0.9925735,0.0000468115,0.00002800349,0.00006489422,0.00005423635,0.0009927981,0.0005487282],"genre_scores_gemma":[0.04040436,0.0001889567,0.9566935,0.00006083346,0.00003043035,0.0001101649,0.0001914729,0.00008681482,0.00223353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004080701,"threshold_uncertainty_score":0.01365137,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2338593764","doi":"10.1016/j.compbiomed.2016.04.006","title":"Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images","year":2016,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Financiadora de Estudos e Projetos; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Magnetic resonance imaging; Texture (cosmology); Compression (physics); Radiology; Artificial intelligence; Computer science; Medicine; Pattern recognition (psychology); Materials science; Image (mathematics); Composite material","authors":[{"name":"Lucas Frighetto-Pereira","is_ca":false},{"name":"Rangaraj M. Rangayyan","is_ca":true},{"name":"Guilherme Augusto Metzner","is_ca":false},{"name":"Paulo Mazzoncini de Azevedo‐Marques","is_ca":false},{"name":"Marcello Henrique Nogueira‐Barbosa","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009458620043709261,"gpt":0.2895267234508843,"spread":0.280068103407175,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009540629,0.0003228838,0.0005531526,0.003413436,0.0002686157,0.0009796087,0.0003699198,0.0006280647,0.0004856407],"category_scores_gemma":[0.003502608,0.0001617295,0.0006710034,0.001140067,0.0004221216,0.000500955,0.0004217057,0.0003935822,0.0002535874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003470314,"about_ca_system_score_gemma":0.0004631957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002675438,"about_ca_topic_score_gemma":0.00245378,"domain_scores_codex":[0.9995974,0.00005600658,0.00005449512,0.00005282755,0.0001751066,0.0000642567],"domain_scores_gemma":[0.9983559,0.0007021206,0.0002116292,0.0001040374,0.0004434414,0.0001828837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004304068,0.0005093584,0.2872588,0.0002660638,0.0003290083,0.0007309184,0.0002447581,0.01816015,0.09817697,0.0005451153,0.002688415,0.5867863],"study_design_scores_gemma":[0.0001172349,0.0008429736,0.4147885,0.00005871649,0.0003758963,0.002303742,0.0006707466,0.5538735,0.02458122,0.001146195,0.001156136,0.00008523479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722745,0.0008287227,0.02508272,0.0001988106,0.0000522855,0.00006340312,0.0006318801,0.0002415042,0.0006262625],"genre_scores_gemma":[0.99134,0.0002302026,0.007565535,0.00002157457,0.00004109543,0.00002506882,0.0004749724,0.0000230019,0.0002785058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003413436,"threshold_uncertainty_score":0.005319655,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385377333","doi":"10.3390/diagnostics13142429","title":"Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future","year":2023,"lang":"en","type":"review","venue":"Diagnostics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Neurosurgery; Principal (computer security); Identification (biology); Epilepsy surgery; Medicine; Realm; Epilepsy; Medical physics; Intensive care medicine; Computer science; Artificial intelligence; Data science; Surgery; Psychiatry; Computer security","authors":[{"name":"Jonathan A. Tangsrivimol","is_ca":false},{"name":"Ethan Schonfeld","is_ca":false},{"name":"Michael Zhang","is_ca":false},{"name":"Anand Veeravagu","is_ca":false},{"name":"Timothy R. Smith","is_ca":false},{"name":"Roger Härtl","is_ca":false},{"name":"Michael T. Lawton","is_ca":false},{"name":"Adham El Sherbini","is_ca":true},{"name":"Daniel M. Prevedello","is_ca":false},{"name":"Benjamin S. Glicksberg","is_ca":false},{"name":"Chayakrit Krittanawong","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04560822427846344,"gpt":0.3135644509611705,"spread":0.267956226682707,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001205235,0.0008615309,0.001453095,0.004651718,0.0004158673,0.002135511,0.0009402108,0.001350573,0.005075067],"category_scores_gemma":[0.002911379,0.000385238,0.0009987843,0.005815671,0.0007314312,0.002187872,0.00087317,0.001518657,0.001519057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009294766,"about_ca_system_score_gemma":0.002794739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00166699,"about_ca_topic_score_gemma":0.003090063,"domain_scores_codex":[0.9995528,0.00008737963,0.000110398,0.00006966671,0.0001458123,0.00003395235],"domain_scores_gemma":[0.9970868,0.002182027,0.0002492333,0.00004670212,0.0003556385,0.00007960498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006479822,0.00006554497,0.000374616,0.0815813,0.0001969542,0.0001936073,0.0002018034,0.0003690391,0.0005591011,0.004996852,0.02904562,0.8823508],"study_design_scores_gemma":[0.00001605603,0.000124233,0.001718922,0.04058339,0.0005449651,0.001262493,0.0002311655,0.0001952296,0.0003218026,0.00439549,0.9505625,0.00004377266],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00007260741,0.9988569,0.00007285871,0.000322664,0.0001411207,0.000003314246,0.0000139498,0.00000473715,0.0005118286],"genre_scores_gemma":[0.0004166108,0.9990216,0.0001312574,0.000150425,0.0001518315,0.000004238977,0.00001386334,0.00000102042,0.0001090642],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005075067,"threshold_uncertainty_score":0.01697779,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2051558301","doi":"10.1016/j.media.2010.07.008","title":"Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Kingston General Hospital; National Research Council Canada; University of British Columbia; Queen's University","funders":"Canadian Institutes of Health Research","keywords":"Imaging phantom; Image registration; Artificial intelligence; Curvature; Computer science; Displacement (psychology); Medicine; Metric (unit); Volume (thermodynamics); Computer vision; Cadaver; Similarity (geometry); Radiology; Mathematics; Image (mathematics); Anatomy","authors":[{"name":"Sean Gill","is_ca":true},{"name":"Purang Abolmaesumi","is_ca":true},{"name":"Gábor Fichtinger","is_ca":true},{"name":"Jonathan Boisvert","is_ca":true},{"name":"David R. Pichora","is_ca":true},{"name":"Dan Borshneck","is_ca":true},{"name":"Parvin Mousavi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.003589663474983332,"gpt":0.229809595028521,"spread":0.2262199315535377,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004115602,0.0003751247,0.0003610078,0.0009104472,0.000251221,0.0008441444,0.0005100337,0.0006526691,0.002932745],"category_scores_gemma":[0.002968361,0.0003589797,0.0004357473,0.0007579239,0.0004543298,0.0004404664,0.0008129222,0.0004778664,0.0007718675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002460306,"about_ca_system_score_gemma":0.001055683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003653221,"about_ca_topic_score_gemma":0.005564181,"domain_scores_codex":[0.9997624,0.00008361223,0.0000162529,0.0000392814,0.00008075215,0.00001771215],"domain_scores_gemma":[0.9996173,0.0001626765,0.00006595715,0.00007006894,0.00006339118,0.00002044577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000846717,0.0003901365,0.005691124,0.0005135929,0.0001514407,0.0004060431,0.0005333688,0.2799544,0.3360625,0.008418637,0.002940767,0.3640913],"study_design_scores_gemma":[0.00004890846,0.0003956086,0.02130092,0.00007545658,0.0001146046,0.0009373298,0.0002492806,0.8769629,0.08606035,0.007741546,0.006048854,0.0000641962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2900092,0.0005000987,0.7047276,0.0003369305,0.0001035075,0.0001516492,0.0003073942,0.0006628277,0.003200865],"genre_scores_gemma":[0.8720483,0.0004885889,0.1221817,0.0001071633,0.00006454033,0.0001336543,0.0003608558,0.0002909909,0.004324177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003653221,"threshold_uncertainty_score":0.009811044,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2513411538","doi":"10.1016/j.media.2016.08.005","title":"Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Segmentation; Hausdorff distance; Artificial intelligence; Computer science; Data set; Ground truth; Magnetic resonance imaging; Pattern recognition (psychology); Intervertebral disc; Computer vision; Medicine; Anatomy; Radiology","authors":[{"name":"Guoyan Zheng","is_ca":false},{"name":"Chengwen Chu","is_ca":false},{"name":"Daniel L. Belavý","is_ca":false},{"name":"Bulat Ibragimov","is_ca":false},{"name":"Robert Korez","is_ca":false},{"name":"Tomaž Vrtovec","is_ca":false},{"name":"Hugo Hutt","is_ca":false},{"name":"Richard Everson","is_ca":false},{"name":"Judith R. Meakin","is_ca":false},{"name":"Isabel Lŏpez Andrade","is_ca":false},{"name":"Ben Glocker","is_ca":false},{"name":"Hao Chen","is_ca":false},{"name":"Qi Dou","is_ca":false},{"name":"Pheng‐Ann Heng","is_ca":false},{"name":"Chunliang Wang","is_ca":false},{"name":"Daniel Forsberg","is_ca":false},{"name":"Aleš Neubert","is_ca":false},{"name":"Jürgen Fripp","is_ca":false},{"name":"Martin Urschler","is_ca":false},{"name":"Darko Štern","is_ca":false},{"name":"M Wimmer","is_ca":false},{"name":"Alexey A. Novikov","is_ca":false},{"name":"Hui Cheng","is_ca":false},{"name":"Gabriele Armbrecht","is_ca":false},{"name":"Dieter Felsenberg","is_ca":false},{"name":"Shuo Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05513416208565318,"gpt":0.4171947585700668,"spread":0.3620605964844136,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01756334,0.002370146,0.003226101,0.005416617,0.0008086844,0.00591863,0.004543361,0.004530595,0.002645463],"category_scores_gemma":[0.02449484,0.001220555,0.002129971,0.002600813,0.001028168,0.003019379,0.00183497,0.00169117,0.001855653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001501736,"about_ca_system_score_gemma":0.002512734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007371385,"about_ca_topic_score_gemma":0.01158192,"domain_scores_codex":[0.99299,0.002036817,0.0007162056,0.001036149,0.002939913,0.0002809008],"domain_scores_gemma":[0.9725611,0.01509367,0.001084393,0.002965647,0.007621854,0.0006733332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009462985,0.000415462,0.007501506,0.001875924,0.001075731,0.0001544059,0.0003482466,0.04988377,0.04191617,0.002589002,0.0124075,0.880886],"study_design_scores_gemma":[0.0001403948,0.001063807,0.01892238,0.0003698899,0.0004244691,0.001099323,0.0007393644,0.901533,0.04767892,0.008107294,0.01969518,0.0002259836],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1124677,0.02931818,0.8366954,0.004149149,0.0008026308,0.0006932152,0.003901376,0.009468142,0.002504211],"genre_scores_gemma":[0.2131971,0.009226838,0.7631143,0.0009281971,0.0003735355,0.0003590045,0.007326629,0.002532391,0.002941972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01756334,"threshold_uncertainty_score":0.09288484,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2103275591","doi":"10.1109/tmi.2002.806578","title":"A knowledge-based approach to automatic detection of the spinal cord in CT images","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Spinal cord; Artificial intelligence; Thorax (insect anatomy); Computer vision; Thresholding; Image processing; Medical imaging; Medicine; Anatomy; Image (mathematics)","authors":[{"name":"Neculai Archip","is_ca":true},{"name":"Pierre‐Jean Erard","is_ca":false},{"name":"M. Egmont‐Petersen","is_ca":false},{"name":"Jacques‐Antoine Haefliger","is_ca":false},{"name":"J.-F. Germond","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01621166980451356,"gpt":0.255441922625736,"spread":0.2392302528212225,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006029144,0.0007567374,0.0007533507,0.002234963,0.0006892094,0.001976189,0.002028714,0.001824654,0.002392301],"category_scores_gemma":[0.002895437,0.000584109,0.001010997,0.001179938,0.0009486774,0.001656598,0.001240873,0.0009906787,0.0009808593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009833089,"about_ca_system_score_gemma":0.001511758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009171923,"about_ca_topic_score_gemma":0.009798322,"domain_scores_codex":[0.9993882,0.0001090622,0.00005377881,0.0001702486,0.0002167848,0.00006188179],"domain_scores_gemma":[0.9992143,0.0004046088,0.00007103526,0.0001280686,0.000149268,0.00003276952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001811302,0.000138103,0.0008464715,0.0002580741,0.00008241466,0.0004671847,0.0003238805,0.07023853,0.04359256,0.00881676,0.003155957,0.8718989],"study_design_scores_gemma":[0.00003816077,0.0001153517,0.002726717,0.00008756373,0.0001427313,0.0008098519,0.0002881223,0.9101378,0.0443515,0.03115573,0.01007326,0.00007323183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007164245,0.0001830052,0.9897428,0.0001217455,0.00001031431,0.00007132092,0.0001172528,0.001858083,0.0007311036],"genre_scores_gemma":[0.1162575,0.0003277054,0.8815781,0.00007626758,0.0000204383,0.0001278409,0.0004898073,0.000102762,0.001019474],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009171923,"threshold_uncertainty_score":0.01823711,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2077007427","doi":"10.1007/bf02344797","title":"Validation of the non-stereo corresponding points stereoradiographic 3D reconstruction technique","year":2001,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre Hospitalier de l’Université de Montréal; École de Technologie Supérieure","funders":"","keywords":"Sagittal plane; Artificial intelligence; 3D reconstruction; Computer vision; Focus (optics); Computer science; Computed tomography; Radiography; Iterative reconstruction; Tomography; Mathematics; Anatomy; Medicine; Radiology; Optics; Physics","authors":[{"name":"Anca Mitulescu","is_ca":false},{"name":"Imad Semaan","is_ca":false},{"name":"Jacques A. de Guise","is_ca":true},{"name":"P. Leborgne","is_ca":false},{"name":"C. Adamsbaum","is_ca":false},{"name":"Wafa Skalli","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00982365829309855,"gpt":0.2180566844061202,"spread":0.2082330261130216,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002647771,0.0005852797,0.000464503,0.001047256,0.0003693739,0.001202844,0.001192134,0.0009995655,0.004829373],"category_scores_gemma":[0.007716474,0.0003657723,0.0005797404,0.0007047219,0.0005272419,0.0007726251,0.000966473,0.000438202,0.001738979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00027039,"about_ca_system_score_gemma":0.001280008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002127162,"about_ca_topic_score_gemma":0.002699212,"domain_scores_codex":[0.9977996,0.0005060639,0.00009196981,0.0002833048,0.001239748,0.00007930963],"domain_scores_gemma":[0.9956124,0.001264445,0.000234125,0.001125393,0.00168837,0.00007526435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001558243,0.0004351949,0.01203496,0.0007127057,0.0003537981,0.0003097709,0.0004122504,0.06937759,0.3954267,0.00781624,0.001913612,0.5096489],"study_design_scores_gemma":[0.0001533011,0.0006245822,0.02319544,0.0000738312,0.0002178241,0.002137784,0.0002067255,0.6867763,0.278078,0.001667149,0.006792587,0.00007651006],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1527403,0.0002197693,0.8401734,0.00009450881,0.0000975788,0.000172031,0.0005320975,0.001754079,0.004216243],"genre_scores_gemma":[0.5329836,0.0002822435,0.4623717,0.00008467906,0.00002359882,0.00008618081,0.001170112,0.0004434307,0.002554482],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004829373,"threshold_uncertainty_score":0.0161559,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2225380751","doi":"10.1007/s10334-015-0507-2","title":"Segmentation of the human spinal cord","year":2016,"lang":"en","type":"review","venue":"Magnetic Resonance Materials in Physics Biology and Medicine","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Spinal cord; Segmentation; Medicine; Cord; White matter; Magnetic resonance imaging; Neuroscience; Artificial intelligence; Computer science; Radiology; Psychology; Surgery","authors":[{"name":"Benjamin De Leener","is_ca":true},{"name":"Manuel Taso","is_ca":false},{"name":"Julien Cohen‐Adad","is_ca":true},{"name":"Virginie Callot","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02994664090366943,"gpt":0.3473927985151322,"spread":0.3174461576114628,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008944593,0.001184876,0.001443302,0.003095687,0.0002321773,0.00124512,0.001339442,0.00147639,0.001353074],"category_scores_gemma":[0.001726845,0.0006228146,0.0006743119,0.002068471,0.0009456506,0.001194127,0.0007538139,0.000866871,0.001300859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006236103,"about_ca_system_score_gemma":0.001766669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00355071,"about_ca_topic_score_gemma":0.004962021,"domain_scores_codex":[0.9996375,0.00004775053,0.00004397945,0.00009813989,0.0001433301,0.00002929015],"domain_scores_gemma":[0.9993731,0.0003079454,0.00009250942,0.00002842173,0.0001705375,0.00002743303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005914499,0.0000254419,0.0002398218,0.009921808,0.0001191111,0.0001292201,0.00004280142,0.001422157,0.004870554,0.001538895,0.009246294,0.9723847],"study_design_scores_gemma":[0.00006339549,0.0002855809,0.007101808,0.008189572,0.0009510336,0.007329504,0.0002046937,0.00930092,0.0277601,0.01411365,0.9245146,0.0001852842],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008197624,0.9891995,0.00758939,0.0003489162,0.0002091141,0.00002001944,0.00007501878,0.00007405596,0.001664134],"genre_scores_gemma":[0.009066548,0.9784662,0.009150734,0.0003434023,0.0004977313,0.00002166787,0.0003047853,0.00003949245,0.002109392],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00355071,"threshold_uncertainty_score":0.007060111,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3183868427","doi":"10.1016/j.nicl.2021.102766","title":"Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning","year":2021,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Canada Foundation for Innovation; National Natural Science Foundation of China; Canada First Research Excellence Fund; Canada Research Chairs; Nvidia; National Science Foundation","keywords":"Segmentation; Spinal cord; Medicine; Deep learning; Lumbar; Minimum bounding box; Cord; Artificial intelligence; Radiology; Computer science; Surgery","authors":[{"name":"Andréanne Lemay","is_ca":true},{"name":"Charley Gros","is_ca":true},{"name":"Zhizheng Zhuo","is_ca":false},{"name":"Jie Zhang","is_ca":false},{"name":"Yunyun Duan","is_ca":false},{"name":"Julien Cohen‐Adad","is_ca":true},{"name":"Yaou Liu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02793730199582332,"gpt":0.3311391495736721,"spread":0.3032018475778487,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008602615,0.001398245,0.001030021,0.001341582,0.0004324018,0.0009177165,0.001494427,0.001527414,0.001237335],"category_scores_gemma":[0.001300377,0.0006730641,0.001281168,0.0009739037,0.0003681584,0.0008492221,0.0009997311,0.001142873,0.000865073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001420098,"about_ca_system_score_gemma":0.00141744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01831684,"about_ca_topic_score_gemma":0.02500159,"domain_scores_codex":[0.9995202,0.00007586707,0.00002891497,0.0001904377,0.00008168603,0.0001028337],"domain_scores_gemma":[0.9995793,0.000132377,0.00005583678,0.00006835148,0.000124599,0.00003953915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007576407,0.0003920815,0.007448962,0.0001897299,0.0002871869,0.000406736,0.0001549693,0.3545045,0.03121253,0.001044527,0.008729781,0.5948714],"study_design_scores_gemma":[0.000008181898,0.00005059916,0.0008655282,0.00001207642,0.00001983416,0.00005270807,0.00001390851,0.9932256,0.004513135,0.0008104641,0.0004178472,0.00001008197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.341842,0.002785213,0.6350421,0.0007289473,0.0002112811,0.000317601,0.001702022,0.01429456,0.003076199],"genre_scores_gemma":[0.8256463,0.0005009763,0.1645079,0.0003829308,0.00006557556,0.0001858735,0.003413494,0.0002695979,0.005027359],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01831684,"threshold_uncertainty_score":0.03642046,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2945719313","doi":"10.1109/tmi.2019.2914400","title":"Toward Automated 3D Spine Reconstruction from Biplanar Radiographs Using CNN for Statistical Spine Model Fitting","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Centre Hospitalier Universitaire Sainte-Justine","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs","keywords":"Convolutional neural network; Artificial intelligence; 3D reconstruction; Radiography; Computer science; Landmark; Iterative reconstruction; Computer vision; Scoliosis; Pattern recognition (psychology); Medicine; Radiology; Surgery","authors":[{"name":"B. Aubert","is_ca":true},{"name":"Carlos Vázquez","is_ca":true},{"name":"Thierry Cresson","is_ca":true},{"name":"Stefan Parent","is_ca":true},{"name":"Jacques A. de Guise","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01573016191745367,"gpt":0.2643584057046334,"spread":0.2486282437871797,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007299723,0.00099471,0.0005831798,0.0008493161,0.0001934508,0.0007834115,0.001222311,0.0007628356,0.001217788],"category_scores_gemma":[0.001633439,0.0008170133,0.001169891,0.000724985,0.0003665929,0.0007573406,0.0008663718,0.0007805225,0.0008738725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007665608,"about_ca_system_score_gemma":0.0009511427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009135146,"about_ca_topic_score_gemma":0.01099976,"domain_scores_codex":[0.9994755,0.00008028207,0.00002373199,0.0001589221,0.0002034745,0.00005804446],"domain_scores_gemma":[0.999498,0.0001472415,0.00008101192,0.0001200489,0.0001343136,0.00001938883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001195748,0.0000662799,0.003517844,0.0001246236,0.0001645165,0.0002226817,0.0001172026,0.5225024,0.05802187,0.00238267,0.002174932,0.4105854],"study_design_scores_gemma":[0.000002354654,0.00001195202,0.0006575093,0.000006019787,0.000008633002,0.00006498128,0.000008240669,0.9925431,0.005576145,0.0005361631,0.000577388,0.000007511792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02466525,0.0001846295,0.9719924,0.00006822858,0.00001756458,0.00003266346,0.0001197196,0.00236369,0.000555894],"genre_scores_gemma":[0.3701206,0.000468072,0.6254973,0.0001343061,0.0000315864,0.0001138595,0.0009724458,0.0004036267,0.002258166],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009135146,"threshold_uncertainty_score":0.01816398,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2165759407","doi":"10.1109/titb.2009.2018286","title":"Texture Analysis for Automatic Segmentation of Intervertebral Disks of Scoliotic Spines From MR Images","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Segmentation; Artificial intelligence; Pattern recognition (psychology); Sagittal plane; Computer science; Computer vision; Intervertebral disk; Feature (linguistics); Anatomy; Medicine","authors":[{"name":"C. Chevrefils","is_ca":true},{"name":"Farida Chériet","is_ca":true},{"name":"Carl‐Éric Aubin","is_ca":true},{"name":"Guy Grimard","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.005795803725425056,"gpt":0.2477866336699634,"spread":0.2419908299445383,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000887355,0.0004659245,0.0008391248,0.002324793,0.0004161655,0.0009764987,0.0006909095,0.0006511947,0.0007775949],"category_scores_gemma":[0.002247472,0.0003139898,0.0007897192,0.001139899,0.0005274495,0.0008858885,0.0004237546,0.0004558749,0.0005493459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004881339,"about_ca_system_score_gemma":0.000698441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002623299,"about_ca_topic_score_gemma":0.00340134,"domain_scores_codex":[0.9992408,0.0001359824,0.00004529404,0.0001312822,0.0003704346,0.00007621871],"domain_scores_gemma":[0.9992905,0.0002491978,0.0001136689,0.00008391227,0.0002258637,0.00003687194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003362312,0.0001094732,0.004148325,0.0003169462,0.0001109033,0.0001839091,0.0001972334,0.02192178,0.2299424,0.002461001,0.00177271,0.7384992],"study_design_scores_gemma":[0.00007846847,0.0002984467,0.02097039,0.00004947803,0.0001364711,0.0009532642,0.0001835156,0.8759697,0.09031045,0.00458975,0.006374021,0.00008601219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04208727,0.0005374918,0.9557184,0.00007706718,0.00002334609,0.00006270324,0.00009435951,0.0009497402,0.0004495746],"genre_scores_gemma":[0.2976439,0.000490881,0.7002307,0.00004367704,0.00004570353,0.000129991,0.0004570795,0.0001890402,0.0007690202],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002623299,"threshold_uncertainty_score":0.005216122,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2114753511","doi":"10.1155/2014/719520","title":"Automatic Labeling of Vertebral Levels Using a Robust Template-Based Approach","year":2014,"lang":"en","type":"article","venue":"International Journal of Biomedical Imaging","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé; National Multiple Sclerosis Society","keywords":"Computer science; Data mining","authors":[{"name":"Eugénie Ullmann","is_ca":true},{"name":"Jean François Pelletier Paquette","is_ca":true},{"name":"William E. Thong","is_ca":true},{"name":"Julien Cohen‐Adad","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02247801149175714,"gpt":0.2636073209538441,"spread":0.241129309462087,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001228685,0.001093264,0.001260903,0.002634008,0.0006976553,0.002281041,0.002365916,0.003088134,0.003010362],"category_scores_gemma":[0.003752232,0.0008684311,0.002232989,0.001820929,0.0006309989,0.0009903144,0.001347853,0.00133147,0.003444423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006889094,"about_ca_system_score_gemma":0.001788823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0045319,"about_ca_topic_score_gemma":0.00784873,"domain_scores_codex":[0.9984033,0.0002227805,0.0001235304,0.0005502608,0.0005771201,0.000123099],"domain_scores_gemma":[0.9986218,0.0003238254,0.000220156,0.0003060793,0.0004626567,0.00006551975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003162205,0.000131811,0.002945967,0.0005433473,0.0003739248,0.0005261935,0.0002223042,0.03973839,0.1844399,0.004258113,0.008325985,0.7581778],"study_design_scores_gemma":[0.00008341176,0.0003282406,0.008493784,0.0001582494,0.0003987374,0.005290766,0.000159931,0.786183,0.1668388,0.01260862,0.01924597,0.0002104259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004716187,0.0003944646,0.9918104,0.00007331558,0.00004481776,0.00009903281,0.0002544425,0.002032546,0.0005748509],"genre_scores_gemma":[0.05771102,0.0003631869,0.9386853,0.0001424428,0.00006659614,0.0001517181,0.0008768256,0.0005678774,0.001435071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0045319,"threshold_uncertainty_score":0.01007068,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046296065","doi":"10.1016/j.media.2012.06.006","title":"Intervertebral disc segmentation in MR images using anisotropic oriented flux","year":2012,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"St Joseph's Health Care; London Health Sciences Centre; Western University; CARE Canada","funders":"","keywords":"Segmentation; Sagittal plane; Artificial intelligence; Intervertebral disc; Computer vision; Computer science; Tracking (education); Image segmentation; Level set (data structures); Active contour model; Pattern recognition (psychology); Anatomy; Medicine","authors":[{"name":"Max W. K. Law","is_ca":true},{"name":"KengYeow Tay","is_ca":true},{"name":"Andrew Leung","is_ca":true},{"name":"Gregory J. Garvin","is_ca":true},{"name":"Shuo Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009771000929835016,"gpt":0.2768342218496146,"spread":0.2670632209197796,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007050239,0.0005278086,0.0004718065,0.001856744,0.0004373715,0.001326289,0.0003467481,0.0009640137,0.001180036],"category_scores_gemma":[0.00126418,0.0003701256,0.000561879,0.0008664668,0.0004083373,0.000792019,0.0002905132,0.0004160024,0.0004220389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003408418,"about_ca_system_score_gemma":0.0007362152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001711311,"about_ca_topic_score_gemma":0.002359787,"domain_scores_codex":[0.9998633,0.00003702851,0.00001268754,0.00002067161,0.00004989777,0.00001648064],"domain_scores_gemma":[0.9997023,0.0001189025,0.00004391838,0.00003003534,0.00008734721,0.00001740993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008325072,0.0001766444,0.004390627,0.0007758411,0.000153302,0.0007151297,0.000493999,0.05733216,0.4154689,0.01238333,0.001918596,0.5053589],"study_design_scores_gemma":[0.00006936817,0.0001888189,0.007485824,0.0001269497,0.0002146731,0.001342704,0.0002186628,0.8404365,0.1328668,0.01002229,0.00696236,0.00006525573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07152301,0.0007334143,0.9251138,0.0002088041,0.00005662046,0.00007627098,0.00008416173,0.0004354524,0.00176849],"genre_scores_gemma":[0.3531077,0.001178055,0.6428979,0.00007935594,0.00009172167,0.00007698435,0.0001538534,0.0002295297,0.002184885],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001856744,"threshold_uncertainty_score":0.003947556,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W133787854","doi":"10.1007/978-3-642-22092-0_19","title":"Graph Cuts with Invariant Object-Interaction Priors: Application to Intervertebral Disc Segmentation","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; CARE Canada","funders":"","keywords":"Prior probability; Computer science; Segmentation; Artificial intelligence; Invariant (physics); Pattern recognition (psychology); Algorithm; Computer vision; Mathematics; Bayesian probability","authors":[{"name":"Ismail Ben Ayed","is_ca":true},{"name":"Kumaradevan Punithakumar","is_ca":true},{"name":"Gregory J. Garvin","is_ca":true},{"name":"Walter Romano","is_ca":true},{"name":"Shuo Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01012386484847342,"gpt":0.235427578069524,"spread":0.2253037132210506,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00218423,0.001224258,0.001509714,0.002003842,0.0005345186,0.001638217,0.002151462,0.002638786,0.001967737],"category_scores_gemma":[0.006258205,0.001219915,0.001258894,0.002489694,0.001183323,0.0009779843,0.001512163,0.002028985,0.0006673183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009817043,"about_ca_system_score_gemma":0.001389371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01002819,"about_ca_topic_score_gemma":0.01247375,"domain_scores_codex":[0.9992064,0.0002937858,0.00003243728,0.0001512477,0.0002601733,0.00005605969],"domain_scores_gemma":[0.9981284,0.001160702,0.0001627901,0.000199809,0.0002689313,0.00007941153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005347527,0.000163838,0.0006750482,0.0002831169,0.0001623017,0.00016381,0.0002115182,0.4737478,0.02204253,0.01513962,0.004603497,0.4822722],"study_design_scores_gemma":[0.00002870756,0.00002577935,0.0003674543,0.00001018037,0.00002460549,0.00005368831,0.00001846652,0.982512,0.003763706,0.0121037,0.001077835,0.00001378627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005890441,0.0001861414,0.9926419,0.0001059251,0.00002351479,0.00003224981,0.0000587893,0.0006624826,0.0003986396],"genre_scores_gemma":[0.119347,0.0004969105,0.8769179,0.0001057095,0.00007980004,0.00007904007,0.0003592339,0.0007759582,0.001838453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01002819,"threshold_uncertainty_score":0.0199396,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2020807249","doi":"10.1097/bpb.0b013e328361ae5b","title":"Three-dimensional imaging of the spine using the EOS system","year":2013,"lang":"en","type":"article","venue":"Journal of Pediatric Orthopaedics B","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Medicine; SPINE (molecular biology); Bioinformatics","authors":[{"name":"Zaid Al-Aubaidi","is_ca":false},{"name":"David E. Lebel","is_ca":true},{"name":"Kamaldine Oudjhane","is_ca":true},{"name":"Reinhard Zeller","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00853738493572601,"gpt":0.2088501945644672,"spread":0.2003128096287412,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002788935,0.0003956436,0.0006760925,0.002433459,0.0001971262,0.0008300735,0.0004139981,0.0004064867,0.00129141],"category_scores_gemma":[0.01032412,0.0003443507,0.0004228729,0.001423417,0.0005964694,0.0007736573,0.000927176,0.0003083866,0.0004337818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001465882,"about_ca_system_score_gemma":0.0002752684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006015951,"about_ca_topic_score_gemma":0.0007852438,"domain_scores_codex":[0.9968741,0.0009715732,0.0005635269,0.0003903827,0.001096041,0.0001043275],"domain_scores_gemma":[0.9921611,0.002761326,0.001448711,0.00140207,0.002084932,0.0001418387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001319478,0.0001162215,0.7602001,0.0004751669,0.0002389865,0.001265438,0.000448245,0.00176479,0.06261931,0.0006882951,0.0006512062,0.1702127],"study_design_scores_gemma":[0.0001095073,0.001930146,0.909304,0.0002327465,0.0003676227,0.02543353,0.000676811,0.01073373,0.04377266,0.0005797346,0.006693743,0.000165748],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9502385,0.002847532,0.04156706,0.00009748546,0.00008629217,0.00009927158,0.0006224109,0.0002338506,0.004207661],"genre_scores_gemma":[0.9601781,0.001214948,0.03733974,0.00008724337,0.00006584954,0.00004535283,0.0005176177,0.00004131139,0.0005098379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002788935,"threshold_uncertainty_score":0.01474947,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385727981","doi":"10.1002/jcsm.13310","title":"A systematic review of automated segmentation of 3D computed‐tomography scans for volumetric body composition analysis","year":2023,"lang":"en","type":"review","venue":"Journal of Cachexia Sarcopenia and Muscle","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University; Memorial University of Newfoundland","funders":"Royal College of Surgeons of England; University of Alberta","keywords":"Segmentation; Artificial intelligence; Computer science; Computed tomography; Ground truth; Pattern recognition (psychology); Medicine; Radiology","authors":[{"name":"Dinh Van","is_ca":false},{"name":"Ioanna Drami","is_ca":false},{"name":"Edward T. Pring","is_ca":false},{"name":"Laura E. Gould","is_ca":false},{"name":"Phillip Lung","is_ca":false},{"name":"Karteek Popuri","is_ca":true},{"name":"Vincent Chow","is_ca":true},{"name":"Mirza Faisal Beg","is_ca":true},{"name":"Thanos Athanasiou","is_ca":false},{"name":"John T. Jenkins","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02065464102461622,"gpt":0.3131872823497296,"spread":0.2925326413251134,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01031986,0.001951783,0.006879039,0.0181387,0.0008080821,0.003316293,0.002908771,0.002085931,0.005646552],"category_scores_gemma":[0.0560532,0.001249392,0.008375153,0.0151127,0.001299156,0.002858425,0.001849588,0.001180993,0.0009418458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003750632,"about_ca_system_score_gemma":0.0123436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009193485,"about_ca_topic_score_gemma":0.0250898,"domain_scores_codex":[0.9898989,0.002728887,0.004328785,0.000886082,0.001989864,0.0001673976],"domain_scores_gemma":[0.9519346,0.03568723,0.006637732,0.0009653378,0.004461656,0.0003134134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000123723,0.000008699778,0.0004334266,0.9321222,0.002953902,0.00008263044,0.0001428929,0.0001097142,0.0001726091,0.0001776419,0.002320583,0.06135193],"study_design_scores_gemma":[0.0001181656,0.0001070022,0.00260238,0.9477586,0.02403518,0.000417004,0.0001682768,0.0001079584,0.0002194297,0.0002507458,0.02417561,0.00003969203],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006867076,0.9968386,0.0005489769,0.0002369496,0.0001501789,0.0003094413,0.0008276554,0.00002587446,0.0003755802],"genre_scores_gemma":[0.007138935,0.9887372,0.002050136,0.0004955195,0.00008312926,0.0006875033,0.0006370082,0.00002827333,0.000142213],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.0181387,"threshold_uncertainty_score":0.05457729,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2023667179","doi":"10.1117/12.2081542","title":"Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Artificial intelligence; Thresholding; Segmentation; Computer science; Computer vision; Vertebra; Image segmentation; Voxel; Pattern recognition (psychology); Deep learning; Image (mathematics); Anatomy; Medicine","authors":[{"name":"Amin Suzani","is_ca":true},{"name":"Abtin Rasoulian","is_ca":true},{"name":"Alexander Seitel","is_ca":true},{"name":"Sidney Fels","is_ca":true},{"name":"Robert Rohling","is_ca":true},{"name":"Purang Abolmaesumi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01012975156175053,"gpt":0.232642526660607,"spread":0.2225127750988564,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000908911,0.0009486407,0.0007577296,0.001342201,0.0004131729,0.0007874873,0.0014233,0.001167156,0.001198345],"category_scores_gemma":[0.002028102,0.0007790857,0.0009112556,0.00112922,0.0006023097,0.0009160566,0.001252625,0.001221768,0.0006843354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162407,"about_ca_system_score_gemma":0.001339945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009833932,"about_ca_topic_score_gemma":0.01525066,"domain_scores_codex":[0.9994824,0.00009946214,0.0000307246,0.0001242652,0.0001864421,0.00007660592],"domain_scores_gemma":[0.9994782,0.0001974143,0.00009113429,0.00008324107,0.000124293,0.00002575832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001473847,0.00011971,0.001575653,0.0001761475,0.00010441,0.0001233658,0.0001099242,0.2875321,0.04903269,0.006168136,0.004521014,0.6503894],"study_design_scores_gemma":[0.000006520904,0.00002309367,0.0004685334,0.00001073884,0.00001086043,0.00003610475,0.000009523468,0.9892426,0.006692962,0.002770911,0.0007203388,0.000007762339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01722671,0.0003945348,0.9794347,0.0001363272,0.00001976972,0.00003751376,0.0001087384,0.002037605,0.0006041229],"genre_scores_gemma":[0.3067273,0.0004946537,0.6890652,0.0002033696,0.00004201606,0.0001391196,0.0006803124,0.0002879937,0.002360156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009833932,"threshold_uncertainty_score":0.01955336,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3094447715","doi":"10.1016/j.media.2020.101872","title":"Unifying neural learning and symbolic reasoning for spinal medical report generation","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Domain (mathematical analysis); Machine learning; Deep learning; Artificial neural network; Segmentation; Graph; Theoretical computer science","authors":[{"name":"Zhongyi Han","is_ca":false},{"name":"Benzheng Wei","is_ca":false},{"name":"Xiaoming Xi","is_ca":false},{"name":"Bo Chen","is_ca":true},{"name":"Yilong Yin","is_ca":false},{"name":"Shuo Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01925856031216853,"gpt":0.2978229118212617,"spread":0.2785643515090932,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001986705,0.000851661,0.0009987097,0.001936953,0.0006972124,0.002612332,0.003041741,0.001481658,0.007354483],"category_scores_gemma":[0.008027521,0.0005515897,0.001875582,0.001274664,0.001189069,0.004138902,0.00284983,0.001835133,0.001689875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001506541,"about_ca_system_score_gemma":0.002961697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01192102,"about_ca_topic_score_gemma":0.02252729,"domain_scores_codex":[0.9983336,0.0003048294,0.0002178146,0.0003859561,0.000573418,0.0001844539],"domain_scores_gemma":[0.9951847,0.002689166,0.0003077549,0.001004146,0.0006840178,0.0001301657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004089979,0.0003957883,0.002650039,0.0003894957,0.0001661918,0.0003613899,0.0002799411,0.2209182,0.009253876,0.04903443,0.007381795,0.7087599],"study_design_scores_gemma":[0.0000187673,0.00003755316,0.0002033975,0.00003434775,0.00003936964,0.00004996315,0.00003576281,0.9376065,0.004981789,0.05512014,0.00185769,0.00001464437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02519803,0.0004658502,0.9620746,0.0006327262,0.00009982815,0.0001688007,0.0006291257,0.007254027,0.003476878],"genre_scores_gemma":[0.4448264,0.000406909,0.5479836,0.0002967039,0.0001086254,0.0001340548,0.001936774,0.0004035632,0.003903327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01192102,"threshold_uncertainty_score":0.02460313,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1978640219","doi":"10.1007/s11548-010-0536-2","title":"Towards accurate, robust and practical ultrasound-CT registration of vertebrae for image-guided spine surgery","year":2010,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research","keywords":"Imaging phantom; Fiducial marker; Image-guided surgery; Image registration; Landmark; Artificial intelligence; Cadaver; Computer vision; Medicine; Computer science; Patient registration; Ultrasound; Medical imaging; Radiology; Surgery; Image (mathematics)","authors":[{"name":"Charles X. B. Yan","is_ca":true},{"name":"Benoît Goulet","is_ca":true},{"name":"Julie Pelletier","is_ca":true},{"name":"Sean Jy-Shyang Chen","is_ca":true},{"name":"Donatella Tampieri","is_ca":true},{"name":"D. Louis Collins","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03085076286241903,"gpt":0.2962677108141484,"spread":0.2654169479517294,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002638642,0.001099369,0.001208357,0.001514769,0.0005388524,0.002401516,0.002062738,0.002948904,0.002010783],"category_scores_gemma":[0.01019082,0.001257428,0.00100306,0.001365719,0.0009546002,0.001668397,0.00231166,0.002187553,0.002583342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005532899,"about_ca_system_score_gemma":0.00176405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002994752,"about_ca_topic_score_gemma":0.00479682,"domain_scores_codex":[0.9973049,0.0007631611,0.0001755808,0.0004188004,0.001229109,0.0001084071],"domain_scores_gemma":[0.9970932,0.00104135,0.0003646175,0.0006353222,0.0007749118,0.00009058588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004738675,0.0001332826,0.002087682,0.0004960102,0.0001480778,0.0001715071,0.0003306859,0.1108781,0.3154282,0.006608886,0.004727808,0.5585158],"study_design_scores_gemma":[0.00004295284,0.0001491691,0.002180763,0.00004492983,0.00006615011,0.0007412705,0.0000864709,0.8941358,0.09236465,0.004313408,0.005808359,0.00006615087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003420994,0.0002323725,0.9951723,0.00007392219,0.00002035524,0.00003147844,0.0000316808,0.0007859332,0.0002309215],"genre_scores_gemma":[0.07722562,0.0003169267,0.9206957,0.00008618356,0.00003372751,0.0000761667,0.0001595368,0.00035111,0.001054901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002994752,"threshold_uncertainty_score":0.01395464,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2138534965","doi":"10.1016/j.medengphy.2015.04.010","title":"Accuracy assessment of 3D bone reconstructions using CT: an intro comparison","year":2015,"lang":"en","type":"article","venue":"Medical Engineering & Physics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"St Joseph's Health Care; Hand and Upper Limb Clinic; Western University","funders":"Canadian Institutes of Health Research","keywords":"Cadaveric spasm; Ground truth; Biomedical engineering; Tomography; 3D reconstruction; Computer science; Artificial intelligence; Medicine; Anatomy; Radiology","authors":[{"name":"Emily Lalone","is_ca":false},{"name":"Ryan Willing","is_ca":false},{"name":"Hannah L. Shannon","is_ca":false},{"name":"Graham J.W. King","is_ca":true},{"name":"James A. Johnson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0341979325293091,"gpt":0.3182790280787495,"spread":0.2840810955494404,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005253035,0.0005819621,0.000460244,0.00232918,0.0002592395,0.00111004,0.0006125853,0.0008399812,0.001061252],"category_scores_gemma":[0.03081975,0.0003728694,0.0005635862,0.0007556645,0.0007325683,0.0006630162,0.001007618,0.0002642943,0.0004297321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004077519,"about_ca_system_score_gemma":0.0003343066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001480923,"about_ca_topic_score_gemma":0.001695671,"domain_scores_codex":[0.9942882,0.001659442,0.0008115626,0.0006206234,0.002479966,0.0001402125],"domain_scores_gemma":[0.976534,0.01387413,0.001528131,0.003473972,0.004475012,0.0001147526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004389887,0.0001843413,0.213349,0.000588085,0.0009499647,0.000584956,0.001624323,0.1529251,0.1388766,0.001862203,0.0008491141,0.4838164],"study_design_scores_gemma":[0.0001391465,0.001962872,0.2545998,0.0002037821,0.0005833023,0.005149181,0.001140855,0.5394542,0.1892406,0.001909496,0.005394073,0.0002227305],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7881951,0.001866617,0.2065505,0.0001378382,0.00007796723,0.00007982073,0.0004205152,0.0007637892,0.001907794],"genre_scores_gemma":[0.951083,0.0003318691,0.04779966,0.0000225647,0.00001457475,0.00001915644,0.0002409651,0.0001313396,0.0003568017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005253035,"threshold_uncertainty_score":0.02778101,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2015898159","doi":"10.1109/tmi.2014.2332571","title":"Local Phase Tensor Features for 3-D Ultrasound to Statistical Shape+Pose Spine Model Registration","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Artificial intelligence; Computer vision; Computer science; Filter (signal processing); Structure tensor; Ultrasound; Phase congruency; 3D ultrasound; Pattern recognition (psychology); Tensor (intrinsic definition); Feature extraction; Gabor filter; Feature (linguistics); Image (mathematics); Mathematics; Radiology; Medicine","authors":[{"name":"Ilker Hacihaliloglu","is_ca":true},{"name":"Abtin Rasoulian","is_ca":true},{"name":"Robert Rohling","is_ca":true},{"name":"Purang Abolmaesumi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01075038529169183,"gpt":0.2880987632157876,"spread":0.2773483779240958,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005761278,0.000613826,0.0004105727,0.0008378796,0.0002031719,0.0006601432,0.000476161,0.0005242717,0.002164129],"category_scores_gemma":[0.002278385,0.000328407,0.0008316993,0.0009824777,0.000456826,0.0007073639,0.0008664931,0.000612032,0.001082738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003826534,"about_ca_system_score_gemma":0.0006989687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001681721,"about_ca_topic_score_gemma":0.002065197,"domain_scores_codex":[0.9996282,0.00009427534,0.00002676647,0.00007114267,0.0001505523,0.00002911084],"domain_scores_gemma":[0.9994672,0.000162556,0.000116789,0.000127956,0.00009623759,0.00002923643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002480983,0.0001032374,0.001077559,0.0002251707,0.00006557574,0.0001967569,0.0001246213,0.1730857,0.166629,0.01806538,0.002700537,0.6374784],"study_design_scores_gemma":[0.00001098886,0.0000891499,0.001557684,0.00001226255,0.00002541029,0.0002724454,0.00002871351,0.9533174,0.03303971,0.007077355,0.004543555,0.00002537385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006753346,0.00009786492,0.9923389,0.00005798924,0.00001587456,0.00002020029,0.00004119188,0.0004189656,0.0002556549],"genre_scores_gemma":[0.2733589,0.000600442,0.7227476,0.00009307444,0.00007554007,0.000131065,0.0004635875,0.000424103,0.002105695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002164129,"threshold_uncertainty_score":0.007239699,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2068854578","doi":"10.1016/j.media.2011.01.006","title":"Automatic inference of articulated spine models in CT images using high-order Markov Random Fields","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine","funders":"Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Markov random field; Context (archaeology); Computer science; Artificial intelligence; Markov chain; Transformation (genetics); Inference; Pattern recognition (psychology); Mathematics; Algorithm; Machine learning; Image (mathematics); Image segmentation","authors":[{"name":"Samuel Kadoury","is_ca":false},{"name":"Hubert Labelle","is_ca":true},{"name":"Nikos Paragios","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01518001686626846,"gpt":0.2554743617237202,"spread":0.2402943448574517,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002319108,0.0008973569,0.001611467,0.001813268,0.0006292432,0.001830335,0.002103787,0.002408685,0.00148701],"category_scores_gemma":[0.009259173,0.002345301,0.002573835,0.001069369,0.001180321,0.001840614,0.001340778,0.003136027,0.0008711432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001231371,"about_ca_system_score_gemma":0.001927493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01207709,"about_ca_topic_score_gemma":0.01898498,"domain_scores_codex":[0.9990906,0.000268846,0.00005767939,0.0002645575,0.0002176185,0.0001008442],"domain_scores_gemma":[0.9948813,0.00382187,0.0004656965,0.0003851561,0.0003054937,0.0001404421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002640025,0.00007983228,0.002131598,0.0001100695,0.000128193,0.0001854815,0.00009380651,0.8768615,0.006546877,0.006896078,0.001318554,0.1053842],"study_design_scores_gemma":[0.000006397875,0.000006994733,0.0001944284,0.00000610362,0.000008806657,0.00002718976,0.000003068197,0.995312,0.0005986123,0.00373639,0.00009316568,0.000006878538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0106009,0.0001806956,0.9881512,0.0001029082,0.00001806028,0.00002017794,0.00007665921,0.0006986099,0.000150846],"genre_scores_gemma":[0.6032478,0.0006966555,0.391398,0.0002333644,0.0001240263,0.0001425735,0.001168555,0.0004982525,0.002490828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01207709,"threshold_uncertainty_score":0.02401358,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2026218910","doi":"10.1016/j.bone.2014.11.023","title":"A comparison of methods for in vivo assessment of cortical porosity in the human appendicular skeleton","year":2014,"lang":"en","type":"article","venue":"Bone","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; Alberta Bone and Joint Health Institute","funders":"Merck Canada; Health Research Board","keywords":"Appendicular skeleton; Porosity; Skeleton (computer programming); Human skeleton; In vivo; Chemistry; Anatomy; Medicine; Biology; Organic chemistry; Biotechnology","authors":[{"name":"Britta Jorgenson","is_ca":true},{"name":"Helen R. Buie","is_ca":true},{"name":"David D. McErlain","is_ca":true},{"name":"Clara Sandino","is_ca":true},{"name":"Steven K. Boyd","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03491787954485751,"gpt":0.4217299062403033,"spread":0.3868120266954458,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002696697,0.0005344814,0.0005977477,0.002633438,0.0002920626,0.001153904,0.0007011644,0.0008415162,0.00128252],"category_scores_gemma":[0.007202205,0.0005393564,0.0003117563,0.001017248,0.0005657584,0.0007785491,0.000593651,0.0005246699,0.0002168602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003851112,"about_ca_system_score_gemma":0.0005027609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001839759,"about_ca_topic_score_gemma":0.002991583,"domain_scores_codex":[0.998924,0.0003379148,0.00006255136,0.000134155,0.0005083846,0.00003302764],"domain_scores_gemma":[0.9941429,0.003565313,0.0005519432,0.000434061,0.001206371,0.00009939076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004848471,0.0003566153,0.04471812,0.002091289,0.0005747421,0.0002669715,0.001453619,0.01254182,0.6131563,0.002338353,0.0005940655,0.3170597],"study_design_scores_gemma":[0.0002718843,0.001796973,0.1966262,0.0003396871,0.0009116939,0.007423206,0.001188362,0.1746657,0.6046518,0.002801544,0.008948821,0.0003740821],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5375692,0.01090875,0.445973,0.0001830502,0.00009923893,0.000211216,0.0006461007,0.0005690543,0.003840291],"genre_scores_gemma":[0.8108175,0.005447782,0.1807427,0.00006732986,0.00005320757,0.0001787955,0.0002902999,0.000302026,0.002100475],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002696697,"threshold_uncertainty_score":0.01426166,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2618758840","doi":"10.1186/s12938-017-0350-y","title":"Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images","year":2017,"lang":"en","type":"article","venue":"BioMedical Engineering OnLine","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Thresholding; Segmentation; Artificial intelligence; Computer science; Preprocessor; Sciatica; Magnetic resonance imaging; Image segmentation; Lumbar; Pattern recognition (psychology); Medicine; Radiology; Image (mathematics)","authors":[{"name":"Maryse Fortin","is_ca":true},{"name":"Mona Omidyeganeh","is_ca":true},{"name":"Michele C. Battié","is_ca":true},{"name":"Omair Ahmad","is_ca":true},{"name":"Hassan Rivaz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03812257313066786,"gpt":0.3422743954869144,"spread":0.3041518223562465,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003556041,0.0006036095,0.0007203419,0.001272561,0.0003921435,0.001078864,0.001108426,0.001042822,0.0009217576],"category_scores_gemma":[0.009610523,0.0003049067,0.0004462433,0.0007347608,0.0003540097,0.0007972164,0.0005314537,0.0004619785,0.0004607365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005268433,"about_ca_system_score_gemma":0.0008343001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001242889,"about_ca_topic_score_gemma":0.001523751,"domain_scores_codex":[0.9978036,0.0005689438,0.0001869594,0.0004093985,0.0009433001,0.00008781901],"domain_scores_gemma":[0.9934958,0.002308971,0.0006259714,0.0003654289,0.003095422,0.0001084314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001352589,0.0004496064,0.02125813,0.0005046744,0.0002725308,0.0002282407,0.0003970412,0.03137066,0.2580149,0.001071875,0.001504607,0.6835751],"study_design_scores_gemma":[0.0001530991,0.001743958,0.04901506,0.00009788365,0.0003175536,0.001352254,0.000190265,0.8066801,0.134978,0.00100439,0.004327657,0.000139714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2787918,0.000740477,0.7171617,0.0001001445,0.0001183902,0.0005232443,0.0001218091,0.001279427,0.00116307],"genre_scores_gemma":[0.2898115,0.0002468702,0.7084408,0.00005106806,0.00003497376,0.0004175905,0.0002099485,0.000164892,0.0006224217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003556041,"threshold_uncertainty_score":0.0188064,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2521928240","doi":"10.1016/j.neuroimage.2016.09.026","title":"Fully-integrated framework for the segmentation and registration of the spinal cord white and gray matter","year":2016,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Heart Institute; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"White matter; Segmentation; Spinal cord; Gray (unit); Diffusion MRI; Artificial intelligence; Computer science; Pattern recognition (psychology); Medicine; Computer vision; Magnetic resonance imaging; Neuroscience; Psychology; Radiology","authors":[{"name":"Sara M. Dupont","is_ca":true},{"name":"Benjamin De Leener","is_ca":true},{"name":"Manuel Taso","is_ca":false},{"name":"Arnaud Le Troter","is_ca":false},{"name":"Sylvie Nadeau","is_ca":true},{"name":"Nikola Stikov","is_ca":true},{"name":"Virginie Callot","is_ca":false},{"name":"Julien Cohen‐Adad","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01437558138984205,"gpt":0.2573354392937131,"spread":0.2429598579038711,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001025477,0.001235578,0.001390849,0.001026837,0.0005090631,0.001581752,0.002751537,0.001978324,0.003059972],"category_scores_gemma":[0.001358249,0.000999236,0.00199102,0.001268119,0.0004797011,0.00116952,0.002063049,0.001601196,0.001473196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008586559,"about_ca_system_score_gemma":0.003703386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02334335,"about_ca_topic_score_gemma":0.04644202,"domain_scores_codex":[0.999485,0.00008793478,0.00002948656,0.0001163604,0.0002023664,0.00007897126],"domain_scores_gemma":[0.999709,0.00006138854,0.00003488579,0.00005881896,0.0001075738,0.00002833816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000491528,0.000249944,0.00134795,0.0003030461,0.0005908223,0.0003066748,0.0001778861,0.4576187,0.03950856,0.01557859,0.009359973,0.4744664],"study_design_scores_gemma":[0.00001443441,0.00003805826,0.0004036428,0.00001137317,0.00004458896,0.0001096172,0.00001214483,0.9887903,0.004538336,0.003738004,0.002280714,0.00001882559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004597647,0.0003770136,0.9927206,0.0001012418,0.00002966553,0.00003696797,0.0002341275,0.001490978,0.0004117357],"genre_scores_gemma":[0.1385782,0.0007492224,0.8532543,0.0002176481,0.000102207,0.0002191222,0.001502892,0.0006891123,0.004687336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02334335,"threshold_uncertainty_score":0.04641497,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2788776605","doi":"10.1007/s12021-018-9365-1","title":"Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning","year":2018,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Science Foundation of Shandong Province","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Multi-task learning; Machine learning; Pattern recognition (psychology); Task (project management)","authors":[{"name":"Zhongyi Han","is_ca":true},{"name":"Benzheng Wei","is_ca":false},{"name":"Stephanie Leung","is_ca":false},{"name":"Ilanit Ben Nachum","is_ca":false},{"name":"David Laidley","is_ca":false},{"name":"Shuo Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008862594629977776,"gpt":0.2281398563315281,"spread":0.2192772617015504,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005785106,0.0006235761,0.0007271268,0.001624363,0.0002857173,0.0009443194,0.0006862608,0.001131615,0.0006852092],"category_scores_gemma":[0.001797163,0.0003102885,0.0006332594,0.0003203206,0.0002403537,0.0005788502,0.0009355101,0.00057659,0.0003799115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002483321,"about_ca_system_score_gemma":0.0005830125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001352144,"about_ca_topic_score_gemma":0.002276945,"domain_scores_codex":[0.9997801,0.00004110479,0.00002038811,0.00006215665,0.00005820885,0.000038096],"domain_scores_gemma":[0.999561,0.0001714887,0.00007175213,0.00004339185,0.0001025388,0.00004972889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001173273,0.0004921465,0.08713502,0.0007042842,0.0002999748,0.003777516,0.0003117141,0.09488058,0.1671795,0.004516839,0.008196734,0.6313324],"study_design_scores_gemma":[0.00003335876,0.0001012692,0.01205639,0.00003923589,0.00009924244,0.001389328,0.00007389807,0.9644619,0.01574607,0.004980945,0.0009924722,0.00002582864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3378641,0.002546929,0.6548642,0.0008475577,0.000115786,0.0001120933,0.0006065181,0.00152004,0.001522905],"genre_scores_gemma":[0.9437265,0.0004965503,0.05444321,0.0001064218,0.0001153867,0.00003573492,0.0003950531,0.0000461677,0.0006348469],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001624363,"threshold_uncertainty_score":0.003059506,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2165854452","doi":"10.1118/1.2746498","title":"Quantitative characterization of metastatic disease in the spine. Part I. Semiautomated segmentation using atlas‐based deformable registration and the level set method","year":2007,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Nuclear Regulatory Commission","keywords":"Atlas (anatomy); Segmentation; Vertebra; Artificial intelligence; Computer science; Image segmentation; Image registration; Pattern recognition (psychology); Data set; Medical imaging; Computer vision; Medicine; Anatomy; Image (mathematics)","authors":[{"name":"Michael Hardisty","is_ca":true},{"name":"Lyle M. Gordon","is_ca":true},{"name":"Praveen Agarwal","is_ca":true},{"name":"T. Skrinskas","is_ca":true},{"name":"Cari Whyne","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05694971904062349,"gpt":0.3391243113823159,"spread":0.2821745923416924,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001783632,0.0005586625,0.000427565,0.00260451,0.0002743411,0.001134966,0.00057382,0.0007932698,0.001291763],"category_scores_gemma":[0.003154509,0.0005411792,0.0006824554,0.0009583402,0.0008220271,0.0008629559,0.0004230727,0.0005049179,0.0004547655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007515906,"about_ca_system_score_gemma":0.0007516286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002369787,"about_ca_topic_score_gemma":0.003092732,"domain_scores_codex":[0.9991738,0.0002049667,0.00005285157,0.00008347978,0.0004607741,0.00002404142],"domain_scores_gemma":[0.9990562,0.0004707474,0.0001241721,0.0001458622,0.000179678,0.00002328356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002668211,0.00009957737,0.008561363,0.0007424155,0.0001563391,0.0002413263,0.0004266221,0.1040934,0.5022652,0.00928612,0.001802223,0.3720585],"study_design_scores_gemma":[0.00005167592,0.0003790072,0.04064097,0.00009973608,0.0001229583,0.002138172,0.0001492456,0.7053717,0.2283677,0.01016513,0.01232604,0.0001876714],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08331598,0.001936877,0.910534,0.0002083029,0.00005858358,0.0002308017,0.0002455107,0.001199419,0.002270475],"genre_scores_gemma":[0.4476234,0.001272063,0.5473117,0.00009447962,0.00004030027,0.0003109165,0.0004425112,0.0003465817,0.002558089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00260451,"threshold_uncertainty_score":0.009432793,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2941681265","doi":"10.1016/j.media.2019.04.012","title":"Direct automated quantitative measurement of spine by cascade amplifier regression network with manifold regularization","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Science and Technology Planning Project of Guangdong Province; China Scholarship Council; National Natural Science Foundation of China","keywords":"Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Computer science; Discriminative model; Embedding; Regularization (linguistics); Cascade; Mathematics; Overfitting; Regression; Nonlinear dimensionality reduction; Curse of dimensionality; Dimensionality reduction; Artificial neural network; Statistics; Engineering","authors":[{"name":"Shumao Pang","is_ca":false},{"name":"Zhihai Su","is_ca":false},{"name":"Stephanie Leung","is_ca":true},{"name":"Ilanit Ben Nachum","is_ca":true},{"name":"Bo Chen","is_ca":true},{"name":"Qianjin Feng","is_ca":false},{"name":"Shuo Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.007527160090647314,"gpt":0.2427483486585076,"spread":0.2352211885678603,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005325878,0.00100519,0.0006737685,0.0007300201,0.0003392355,0.0006305712,0.0009334611,0.0009130895,0.00180759],"category_scores_gemma":[0.001214176,0.0004407919,0.0005572522,0.0006727194,0.0003377805,0.0009190496,0.0008237318,0.0006895392,0.000879455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004141004,"about_ca_system_score_gemma":0.0007859673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002457294,"about_ca_topic_score_gemma":0.00514458,"domain_scores_codex":[0.9995567,0.00007661432,0.00001426338,0.0001387014,0.0001724115,0.00004128451],"domain_scores_gemma":[0.9996827,0.00007682541,0.00004200374,0.00004695442,0.0001381664,0.00001352323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003631358,0.0001602776,0.004294686,0.0003006091,0.0001680595,0.0001906886,0.0002017118,0.08339628,0.2946496,0.005053001,0.004157519,0.6070644],"study_design_scores_gemma":[0.00001413269,0.0001421939,0.003717206,0.00002142889,0.00006773705,0.0003207562,0.00002996309,0.9405867,0.05047179,0.002799853,0.001788948,0.00003927048],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02933162,0.0002718372,0.9674646,0.0001211988,0.00004327495,0.00005091523,0.00009211207,0.001132336,0.00149203],"genre_scores_gemma":[0.5420834,0.0004547296,0.4525623,0.0001380333,0.00007075758,0.0001604026,0.0002716011,0.0002455123,0.004013308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002457294,"threshold_uncertainty_score":0.00604701,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2305452864","doi":"10.1016/j.compbiomed.2016.03.018","title":"Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images","year":2016,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shadow (psychology); Process (computing); Ultrasound; Segmentation; Computer science; Artificial intelligence; Computer vision; Spinous process; Acoustic shadow; Anatomy; Medicine; Radiology; Psychology","authors":[{"name":"Florian Berton","is_ca":true},{"name":"Farida Chériet","is_ca":true},{"name":"Marie‐Claude Miron","is_ca":true},{"name":"Catherine Laporte","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.007064974271652483,"gpt":0.2713011765168071,"spread":0.2642362022451546,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000340864,0.0003116204,0.0002608971,0.002231478,0.0002961053,0.001274698,0.0002876903,0.0009604407,0.001720601],"category_scores_gemma":[0.00117911,0.0003166216,0.0003534848,0.0008627471,0.0003947126,0.0003429941,0.0003330993,0.0004387284,0.0005945051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002632198,"about_ca_system_score_gemma":0.001033935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006595911,"about_ca_topic_score_gemma":0.006259726,"domain_scores_codex":[0.9998565,0.00001562868,0.00001342712,0.00002307895,0.00005580275,0.00003555],"domain_scores_gemma":[0.9996696,0.0001398273,0.00003453612,0.00003908032,0.00007034653,0.00004669894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008253175,0.00009719114,0.01474526,0.000447681,0.00008038694,0.002452629,0.0009144424,0.01581172,0.4798239,0.002737059,0.001493589,0.4805708],"study_design_scores_gemma":[0.0001196808,0.0003979261,0.1612789,0.0002658085,0.0004148352,0.01112664,0.00141296,0.5025719,0.3019559,0.006488442,0.0138299,0.000137128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6753417,0.002070384,0.3144307,0.0005259598,0.00008837985,0.0002213064,0.0004832973,0.001383278,0.005454895],"genre_scores_gemma":[0.8800749,0.001034245,0.1151036,0.0001080734,0.00009121808,0.00003797052,0.000346829,0.0001712286,0.003031943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006595911,"threshold_uncertainty_score":0.01311505,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}