{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2749,"total_is_capped":false,"direct_labels_cover":4,"predictions_cover":2749,"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"},"query_hash":"eda52f82d563","filters":{"topic":"Medical Imaging Techniques and Applications"}},"results":[{"id":"W2019607817","doi":"10.1016/j.ejca.2008.10.026","title":"New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)","year":2008,"lang":"en","type":"article","venue":"European Journal of Cancer","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":29472,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"Medical Research Council","keywords":"Guideline; Response Evaluation Criteria in Solid Tumors; Medicine; Clinical trial; Clinical endpoint; Pathological; Disease; Target lesion; Progressive disease; Medical physics; Radiology; Pathology; Internal medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.07009081787729322,"gpt":0.4234162029133073,"spread":0.3533253850360141,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003338796,0.00008292982,0.0002146615,0.0001531327,0.00004023718,0.000009398826,0.0001304469,0.00001668993,0.001507797],"category_scores_gemma":[0.0009544573,0.0000658615,0.00007599971,0.000204913,0.00004163261,0.00007932893,0.00002472721,0.0002549995,0.00002834322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001688444,"about_ca_system_score_gemma":0.0004988068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002309201,"about_ca_topic_score_gemma":0.000001224738,"domain_scores_codex":[0.9981853,0.0004163523,0.0006670492,0.0001125459,0.0004992149,0.0001195323],"domain_scores_gemma":[0.9987555,0.00004081773,0.0002886184,0.0001896725,0.0005186165,0.000206748],"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":[0.001266459,0.00005243277,0.0008516481,0.000007820003,0.00001155414,0.0003499516,0.0002149329,0.00001846471,0.04834016,0.000002943797,0.891074,0.05780962],"study_design_scores_gemma":[0.004574703,0.0004857828,0.05670002,0.001352956,0.0001374162,0.0006913028,0.00004186806,0.001704857,0.006503518,0.00005528775,0.9276159,0.0001364145],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8229696,0.004322436,0.02335219,0.1390579,0.0005033322,0.0006284682,0.000007464202,0.00006684991,0.009091849],"genre_scores_gemma":[0.926128,0.001719917,0.06467466,0.002953544,0.001225706,0.000003650045,0.000007973092,0.00003719671,0.003249377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1361043,"threshold_uncertainty_score":0.999405,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2020745232","doi":"10.1016/j.neuroimage.2010.07.033","title":"Unbiased average age-appropriate atlases for pediatric studies","year":2010,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":2455,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.06350428654610493,"gpt":0.3698605340946254,"spread":0.3063562475485204,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001734457,0.000131779,0.0002379834,0.00007324004,0.0001066062,0.00002092507,0.0001185694,0.00005606284,0.00006163748],"category_scores_gemma":[0.0008000784,0.0001040618,0.0001054428,0.0001673955,0.0001227606,0.00004346582,0.00005525161,0.000335626,0.00003869537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008505329,"about_ca_system_score_gemma":0.00004224436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005876742,"about_ca_topic_score_gemma":0.000002564875,"domain_scores_codex":[0.9990754,0.00001388778,0.0002124913,0.0003020891,0.0001687974,0.0002273521],"domain_scores_gemma":[0.9990184,0.0002155871,0.00006555799,0.0004271948,0.0001199418,0.000153269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001692589,0.0008288978,0.005311159,0.0007155712,0.00006737284,0.0003945271,0.000202994,0.000001188683,0.6104738,0.005518985,0.3591961,0.01712014],"study_design_scores_gemma":[0.009943894,0.002132518,0.04848045,0.0001490535,0.001534108,0.0004418896,0.0001080489,0.00818091,0.1315825,0.02345884,0.7725378,0.001449995],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795715,0.00009110087,0.006889518,0.009195161,0.0003837282,0.001369568,0.0000343891,0.000621993,0.001843048],"genre_scores_gemma":[0.9370685,0.0002070173,0.05715339,0.002499279,0.0008594824,0.0003167693,0.00003669878,0.00004252887,0.00181638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4788913,"threshold_uncertainty_score":0.4243518,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2138884296","doi":"10.1088/0031-9155/49/19/007","title":"GATE: a simulation toolkit for PET and SPECT","year":2004,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":2127,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre for Addiction and Mental Health","funders":"National Institute of Biomedical Imaging and Bioengineering; National Science Foundation; National Cancer Institute; Vlaamse regering; Fonds Wetenschappelijk Onderzoek; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Modular design; Computer science; Detector; Flexibility (engineering); Gate array; Monte Carlo method; Field (mathematics); Computer engineering; Simulation; Computer hardware; Field-programmable gate array; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.247207432402157,"gpt":0.4749278160885859,"spread":0.2277203836864289,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001356656,0.00006409729,0.0001890022,0.00003270242,0.00002713213,0.000001803977,0.00002255058,0.00002607808,0.000007480408],"category_scores_gemma":[0.0001207662,0.00004392895,0.00001268255,0.00007864929,0.0001982045,0.00001591433,0.00001669739,0.00009201134,6.604556e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000127867,"about_ca_system_score_gemma":0.00001731473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005055187,"about_ca_topic_score_gemma":0.000003068368,"domain_scores_codex":[0.9995772,0.000006897702,0.0001257244,0.000154679,0.00003046943,0.0001050613],"domain_scores_gemma":[0.9996828,0.0001219955,0.00002806108,0.00008629986,0.00002583121,0.00005494103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003722553,0.0006035671,0.02425189,0.0006721852,0.00008700152,0.00003913096,0.001289793,0.0001863524,0.08930799,0.6678233,0.004258196,0.2111084],"study_design_scores_gemma":[0.0098071,0.002223243,0.008069831,0.0005807986,0.0001761004,0.0001504845,0.0002596366,0.02902059,0.002207181,0.8926494,0.05456062,0.000295019],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8203943,0.0004189881,0.1317213,0.04539688,0.00005689131,0.0008014759,0.000007980017,0.00007404252,0.001128086],"genre_scores_gemma":[0.9888343,0.000288089,0.00869401,0.00174277,0.0003252474,0.00004006405,0.00004613773,0.00000541695,0.00002397403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2248261,"threshold_uncertainty_score":0.1791371,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2148234126","doi":"10.1088/0031-9155/46/5/201","title":"Three-dimensional ultrasound imaging","year":2001,"lang":"en","type":"review","venue":"Physics in Medicine and Biology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":783,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; Robarts Clinical Trials","funders":"","keywords":"Ultrasound; Modality (human–computer interaction); 3D ultrasound; Computer science; Ultrasound imaging; Flexibility (engineering); Medical diagnosis; Medical physics; Medical imaging; Artificial intelligence; Imaging technology; Computer vision; Radiology; Medicine; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.2839347018507447,"gpt":0.4793291109828301,"spread":0.1953944091320854,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002962572,0.0002836459,0.001470006,0.0001322059,0.00004230703,0.000003565453,0.0001158966,0.0001699603,0.0001105875],"category_scores_gemma":[0.0001516983,0.0001750287,0.0001100126,0.0003816965,0.0005515424,0.00001653973,0.00007342313,0.0006712122,0.00001441205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004137037,"about_ca_system_score_gemma":0.0001047938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001424082,"about_ca_topic_score_gemma":0.000005660848,"domain_scores_codex":[0.9986845,0.00004054799,0.0004726254,0.0004335915,0.00009776653,0.0002709806],"domain_scores_gemma":[0.9988841,0.0004677633,0.0001463358,0.0003224177,0.0000424329,0.0001369745],"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":[0.000002514056,0.00006318133,0.0003171089,0.001428941,0.00003137044,0.00002402328,0.000007176055,1.059716e-8,0.00002071925,0.00434199,0.01231243,0.9814506],"study_design_scores_gemma":[0.0003430339,0.00007159713,0.00001491143,0.008094708,0.0003945398,0.0005048811,0.000004532199,0.00007299823,5.439616e-7,0.01365383,0.9766858,0.0001586156],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001465367,0.9930722,0.002673763,0.002192757,0.00009557632,0.0005242016,0.000009714046,0.00006430558,0.001352872],"genre_scores_gemma":[0.00004244638,0.9945367,0.002438576,0.001219146,0.001122864,0.0001066512,0.0004516724,0.00002747439,0.00005444195],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9812919,"threshold_uncertainty_score":0.7137462,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2911290743","doi":"10.1073/pnas.1907377117","title":"On instabilities of deep learning in image reconstruction and the potential costs of AI","year":2020,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":759,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Fundação para a Ciência e a Tecnologia; Natural Sciences and Engineering Research Council of Canada; European Commission; Research Councils UK; Royal Society; Engineering and Physical Sciences Research Council; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Leverhulme Trust; Nvidia","keywords":"Counterintuitive; Deep learning; Artificial intelligence; Computer science; Image (mathematics); Instability; Stability (learning theory); Iterative reconstruction; Field (mathematics); Sampling (signal processing); Computer vision; Machine learning; Mathematics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.02349296957277632,"gpt":0.3122047471846728,"spread":0.2887117776118965,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007719711,0.00003564607,0.0001288336,0.0000617205,0.00004380745,0.000003832011,0.0001515237,0.00003012788,0.000009869469],"category_scores_gemma":[0.001391319,0.00001966685,0.00003240195,0.0003320654,0.00176097,0.0000990153,0.00005139571,0.000194218,6.770733e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001020971,"about_ca_system_score_gemma":0.00001874255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001085036,"about_ca_topic_score_gemma":2.984587e-8,"domain_scores_codex":[0.9991655,0.000006898839,0.0002439291,0.0000948633,0.0004420856,0.00004670254],"domain_scores_gemma":[0.9994707,0.0001220491,0.0002255893,0.000004890862,0.0001574266,0.00001931893],"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":[0.0002054045,0.0000666889,0.01447557,0.0003131197,0.0000133635,6.248149e-9,0.001001538,0.0000566961,0.6629937,0.3114793,0.0002510281,0.009143499],"study_design_scores_gemma":[0.001809091,0.0003259761,0.06844246,0.0007950141,0.00005070885,0.0000296536,0.002873892,0.09080582,0.649829,0.1848678,0.00007601052,0.00009458411],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9577798,0.00003678025,0.00002175819,0.04004209,0.000003178834,0.0001797217,0.000002087565,0.000005966283,0.00192865],"genre_scores_gemma":[0.9963892,0.00004364084,0.003150912,0.0003841459,0.00001726162,0.000006284076,6.401196e-8,0.000001351793,0.000007140458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1266115,"threshold_uncertainty_score":0.6488369,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2104307326","doi":"10.1016/j.bone.2007.07.007","title":"Automatic segmentation of cortical and trabecular compartments based on a dual threshold technique for in vivo micro-CT bone analysis","year":2007,"lang":"en","type":"article","venue":"Bone","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":601,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"In vivo; Cortical bone; Segmentation; Dual (grammatical number); Trabecular bone; Biomedical engineering; Medicine; Computer science; Biology; Artificial intelligence; Anatomy; Pathology; Osteoporosis; Art; Biotechnology","retraction":null,"screen_n_in":null,"score":{"opus":0.02260748397802071,"gpt":0.3396893925067764,"spread":0.3170819085287557,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005464331,0.00008578577,0.0003196524,0.0002549087,0.00002602551,0.000004724491,0.00002548203,0.00003795041,0.0000578986],"category_scores_gemma":[0.00005062785,0.00007490545,0.00007690097,0.0003523157,0.00007723839,0.00001529184,0.000009520868,0.00009351302,6.13829e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004353285,"about_ca_system_score_gemma":0.0000239116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001973908,"about_ca_topic_score_gemma":0.00001015589,"domain_scores_codex":[0.9991039,0.00001369678,0.0003814866,0.0001697445,0.0001855489,0.000145598],"domain_scores_gemma":[0.9994906,0.0001067462,0.00008212141,0.000193232,0.0000329806,0.00009430885],"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":[0.0001406699,0.001007955,0.01595508,0.0002263543,0.00008353216,0.00005010857,0.00005358533,0.000006812198,0.9792877,0.0005440402,0.001192467,0.001451684],"study_design_scores_gemma":[0.002860388,0.000451981,0.04556539,0.0002933489,0.0008031789,0.00003287312,0.00005883081,0.07896256,0.8701917,0.0003299485,0.0002995991,0.0001501821],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8322228,0.00003202255,0.1655579,0.0008145236,0.000004683869,0.001174982,0.00001879338,0.00004284448,0.0001313869],"genre_scores_gemma":[0.8883867,0.000005185932,0.1109508,0.0003654391,0.00000896615,0.0001985673,0.00004885951,0.000008463408,0.00002698043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.109096,"threshold_uncertainty_score":0.3054555,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1989936418","doi":"10.1007/s12350-010-9246-y","title":"Single photon-emission computed tomography","year":2010,"lang":"en","type":"article","venue":"Journal of Nuclear Cardiology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":598,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Michael's Hospital","funders":"","keywords":"Medicine; Single-photon emission computed tomography; Computed tomography; Nuclear medicine; Tomography; Radiology; Medical physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01774435286717424,"gpt":0.2876126241455387,"spread":0.2698682712783645,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002511728,0.00006401363,0.0003040529,0.0001145287,0.00003861939,0.000007002819,0.0001116979,0.0001174572,0.0001306718],"category_scores_gemma":[0.0001077699,0.00004617457,0.0002161093,0.0001157876,0.0001263763,0.00002663677,0.00003172737,0.0005718294,0.000009985328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001170925,"about_ca_system_score_gemma":0.00003093291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001660658,"about_ca_topic_score_gemma":5.914081e-8,"domain_scores_codex":[0.9993885,0.00002580021,0.0002550819,0.00008150409,0.0001321032,0.0001170235],"domain_scores_gemma":[0.9992833,0.00004105335,0.0001402255,0.0001826617,0.0001603287,0.0001924075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005115212,0.00008262187,0.002716877,0.00001026543,0.00006139094,0.000115481,0.00002681843,3.608726e-7,0.8512743,0.0004183719,0.1397697,0.005472657],"study_design_scores_gemma":[0.0006748754,0.0005316511,0.01362439,0.00005631099,0.0001034789,0.005935972,0.00002600305,0.0003344623,0.005635091,0.0006564792,0.9723477,0.00007354638],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822054,0.00005357917,0.001352229,0.009219365,0.0004163769,0.0000992135,0.000001389835,0.00006392593,0.006588531],"genre_scores_gemma":[0.9635666,0.00002328606,0.03465787,0.001114068,0.0005886175,6.62189e-7,0.00000173347,0.00001367975,0.00003349253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8456392,"threshold_uncertainty_score":0.2484346,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2076560915","doi":"10.1097/00004647-200106000-00002","title":"Positron Emission Tomography Compartmental Models","year":2001,"lang":"en","type":"review","venue":"Journal of Cerebral Blood Flow & Metabolism","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":519,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Positron emission tomography; Radioligand; Impulse (physics); Binding potential; Impulse response; Physics; Context (archaeology); Macro; Computer science; Nuclear medicine; Mathematics; Mathematical analysis; Medicine; Receptor; Biology; Internal medicine; Classical mechanics","retraction":null,"screen_n_in":null,"score":{"opus":0.0452836044227152,"gpt":0.3461500863467626,"spread":0.3008664819240474,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005371872,0.0005419744,0.003233419,0.0005837341,0.000100478,0.00005774604,0.0004789144,0.0003739879,0.0002755301],"category_scores_gemma":[0.00004311094,0.0003547713,0.002016435,0.0006917772,0.0001119871,0.0002125926,0.0001056439,0.001487786,0.00001475123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000545702,"about_ca_system_score_gemma":0.000368508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004852083,"about_ca_topic_score_gemma":1.180154e-7,"domain_scores_codex":[0.9965106,0.0001565586,0.001686209,0.0003337439,0.0008876549,0.0004252149],"domain_scores_gemma":[0.9971823,0.00007426171,0.001205619,0.0005303076,0.0002490812,0.0007584443],"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":[0.00004849825,0.001506581,0.00001609379,0.003984182,0.001535635,0.0002866522,0.00003974686,0.000001646424,0.00008143218,0.0006723531,0.08513971,0.9066875],"study_design_scores_gemma":[0.001235043,0.0001651625,0.000006831464,0.01049303,0.01176218,0.004982047,0.000005545919,0.0001769671,0.00008732265,0.0009413122,0.9698519,0.0002926847],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001540658,0.9948539,0.00169084,0.0006744928,0.0003832954,0.0008015222,0.00004674966,0.00007528385,0.00131982],"genre_scores_gemma":[0.00006386989,0.9531013,0.04390033,0.0003523781,0.001849158,0.00003330661,0.0001073045,0.0000818121,0.0005105906],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9063948,"threshold_uncertainty_score":0.9998904,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2126393454","doi":"10.1109/tmi.2011.2158349","title":"Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":442,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"School of Medicine, Stanford University; Engineering and Physical Sciences Research Council; Leids Universitair Medisch Centrum; Universiteit Leiden","keywords":"Image registration; Computer science; Computer vision; Medical imaging; Artificial intelligence; Computed tomography; Medical physics; Radiology; Medicine; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.1616580525536176,"gpt":0.4636104541303798,"spread":0.3019524015767622,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004892809,0.0001642433,0.0002433121,0.0001465731,0.0001701685,0.000009935744,0.0002186349,0.00006720443,0.001498032],"category_scores_gemma":[0.0003369351,0.0001107293,0.0001522841,0.000287802,0.0003726533,0.00007763781,0.000002162811,0.0006995521,0.00003044225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008830993,"about_ca_system_score_gemma":0.0002818498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009970522,"about_ca_topic_score_gemma":0.000009869357,"domain_scores_codex":[0.9968362,0.00044644,0.0004684907,0.0003180882,0.001714488,0.0002162475],"domain_scores_gemma":[0.9983745,0.0002886546,0.0001408496,0.0006419005,0.0003182389,0.0002358396],"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":[0.00004824921,0.000739517,0.00001978434,0.00003907006,0.00005490868,0.00001069362,0.000441038,0.00001042689,0.001510191,0.0004551412,0.0008705897,0.9958004],"study_design_scores_gemma":[0.003165158,0.0007873315,0.001693259,0.002073055,0.003039282,0.0005531785,0.001347703,0.4377794,0.5253358,0.01316067,0.01040867,0.0006565331],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01079453,0.0001397592,0.9462485,0.02378198,0.0002965246,0.0008064431,0.000005104421,0.0002027197,0.01772449],"genre_scores_gemma":[0.9764431,0.0001851908,0.02131226,0.001560055,0.00008835714,0.0002680465,0.000004459895,0.00002603778,0.0001124671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9951438,"threshold_uncertainty_score":0.9994147,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2174753847","doi":"10.1093/brain/awv112","title":"Existing Pittsburgh Compound-B positron emission tomography thresholds are too high: statistical and pathological evaluation","year":2015,"lang":"en","type":"article","venue":"Brain","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":433,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Institute on Aging; Tau Consortium; Canadian Institutes of Health Research; Hellman Family Foundation","keywords":"Pittsburgh compound B; Positron emission tomography; Amyloid (mycology); Nuclear medicine; Positron emission; Dementia; Alzheimer's disease; Brain positron emission tomography; Neuroimaging; Psychology; Medicine; Pathology; Preclinical imaging; Neuroscience; In vivo; Disease; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.1029160992891289,"gpt":0.3841546332085716,"spread":0.2812385339194426,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009720618,0.00010915,0.0001997552,0.00005744292,0.00008012345,0.00002922596,0.00005848779,0.00009595456,0.00005091498],"category_scores_gemma":[0.00088349,0.00008349048,0.00002793828,0.0001223666,0.0001576128,0.00003970772,0.00006171782,0.0002001298,0.000003816739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004656149,"about_ca_system_score_gemma":0.00005440731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003050262,"about_ca_topic_score_gemma":6.323728e-7,"domain_scores_codex":[0.9988297,0.0000946488,0.0002106153,0.0002756946,0.000411785,0.0001775558],"domain_scores_gemma":[0.9990858,0.0001674422,0.00006699135,0.0002126724,0.0001334334,0.0003336665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002528606,0.0007466048,0.1677132,0.0001266528,0.00003766779,0.0002498185,0.0004255592,0.000002589193,0.04417517,0.03236876,0.6812367,0.07266439],"study_design_scores_gemma":[0.006885091,0.001546957,0.720273,0.0007561389,0.0004514954,0.001126892,0.0005576938,0.02557007,0.002538833,0.1745033,0.06499363,0.0007968883],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9089006,0.0002544324,0.06018282,0.02760222,0.00005892902,0.0006598797,0.00001509561,0.0002562045,0.00206988],"genre_scores_gemma":[0.9634078,0.000007729929,0.03436621,0.001784888,0.0001293991,0.00006498642,0.000112309,0.00001152437,0.0001151681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6162431,"threshold_uncertainty_score":0.3404643,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2150903265","doi":"10.1016/j.media.2013.05.008","title":"Medical image processing on the GPU – Past, present and future","year":2013,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":405,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Vetenskapsrådet","keywords":"Computer science; Image processing; Graphics processing unit; Medical imaging; Artificial intelligence; Computer vision; Histogram; General-purpose computing on graphics processing units; Interpolation (computer graphics); Graphics; Computer graphics; Image registration; Computer graphics (images); Image (mathematics); Parallel computing","retraction":null,"screen_n_in":null,"score":{"opus":0.03209868690587763,"gpt":0.3864913773342081,"spread":0.3543926904283305,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001766116,0.0006675932,0.002761077,0.0004668447,0.0002756138,0.0001971141,0.0008903358,0.0008700231,0.0138912],"category_scores_gemma":[0.0009361858,0.0003299824,0.001138851,0.002030048,0.001174915,0.00009530483,0.0004246667,0.00255981,0.0003035779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008595035,"about_ca_system_score_gemma":0.0006669224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006614589,"about_ca_topic_score_gemma":0.000002522216,"domain_scores_codex":[0.9937102,0.0003877532,0.001217419,0.0009950493,0.003074428,0.0006151573],"domain_scores_gemma":[0.9956487,0.0007579516,0.0004300557,0.001300755,0.0002428064,0.001619744],"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":[0.000002212818,0.0001943012,0.000003894505,0.002731241,0.0008384433,0.0002107743,0.00002080366,1.830255e-9,7.33156e-7,0.00009593969,0.1944552,0.8014465],"study_design_scores_gemma":[0.0002016631,0.00004774392,0.00001044442,0.004995549,0.01056416,0.0001975089,0.000041442,0.002495453,0.000002452165,0.00005855529,0.9810753,0.0003097476],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000006002646,0.8201616,0.007655297,0.1667378,0.00005644267,0.001362471,0.00002220953,0.0002580944,0.003740065],"genre_scores_gemma":[0.000007772175,0.9832816,0.004707167,0.003833929,0.005459144,0.0009496653,0.0003634175,0.00008386511,0.001313506],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8011367,"threshold_uncertainty_score":0.9999152,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3003650457","doi":"10.1148/ryai.2020190007","title":"fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":401,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Artificial intelligence; Art history; Art; Humanities; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.1295735737230169,"gpt":0.3594651300221773,"spread":0.2298915562991604,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003568331,0.0001381036,0.000356177,0.00008026133,0.0001476374,0.00002883456,0.0001090905,0.0001105863,0.0004929794],"category_scores_gemma":[0.0008815393,0.0001256909,0.00004704139,0.0002641488,0.0005026236,0.0001444068,0.00007079042,0.0003201491,0.00002029504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002071772,"about_ca_system_score_gemma":0.0000727988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001589572,"about_ca_topic_score_gemma":0.00000414129,"domain_scores_codex":[0.9987672,0.00007036019,0.0004574792,0.000390679,0.00008240376,0.0002318792],"domain_scores_gemma":[0.9991003,0.0001886965,0.0001885572,0.0002031558,0.0001451084,0.0001742356],"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":[0.0002965389,0.00008118663,0.00355523,0.0001506117,0.00005852654,0.000008231238,0.0001513072,0.00004480132,0.9559379,0.00355622,0.01090622,0.0252532],"study_design_scores_gemma":[0.0001564588,0.0004741427,0.00004142353,0.00006221389,0.0001164494,0.0002787017,0.0002245971,0.5353735,0.4496086,0.001488365,0.01200086,0.0001747161],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.195082,0.0003815448,0.7913899,0.01139174,0.00009068091,0.0009254267,0.0003865148,0.000189919,0.0001622388],"genre_scores_gemma":[0.737111,0.0003254587,0.2605596,0.0007999019,0.0002556732,0.00006855874,0.0007727551,0.00003416309,0.00007284572],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.542029,"threshold_uncertainty_score":0.5397779,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2098533585","doi":"10.1118/1.1915012","title":"Accuracy of finite element model‐based multi‐organ deformable image registration","year":2005,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":374,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Varian Medical Systems","keywords":"Image registration; Finite element method; Deformation (meteorology); Process (computing); Tracking (education); Computer vision; Position (finance); Surface (topology); Computer science; Artificial intelligence; Medical imaging; Image (mathematics); Mathematics; Geometry; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.03665291933969574,"gpt":0.3497256431557559,"spread":0.3130727238160602,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003032474,0.0001156678,0.0002252537,0.00002703891,0.00004987635,0.000007782996,0.0001513971,0.00008209177,0.0002627518],"category_scores_gemma":[0.0004872711,0.00009344354,0.00008659731,0.0001675623,0.0001770889,0.0001036466,0.00003987211,0.000281379,0.00003403682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005317028,"about_ca_system_score_gemma":0.0003301581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002495489,"about_ca_topic_score_gemma":0.000003703092,"domain_scores_codex":[0.9985234,0.00001350398,0.0003998787,0.000182094,0.0006737037,0.0002073786],"domain_scores_gemma":[0.99895,0.000097564,0.0001504385,0.0003981911,0.0001279166,0.0002758845],"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":[0.0002358293,0.008568375,0.0018639,0.001304424,0.0001492804,0.00003795913,0.0006957484,0.003276905,0.09742945,0.01349367,0.1794735,0.693471],"study_design_scores_gemma":[0.001115032,0.00006072381,0.00005085284,0.0001417854,0.00004720426,0.000002592359,0.000006249613,0.858602,0.1294752,0.0007995834,0.009608606,0.00009018007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007711791,0.00002605277,0.9718745,0.01806083,0.00001554369,0.0003377124,0.00001217316,0.0001319791,0.001829429],"genre_scores_gemma":[0.8602889,0.00005572363,0.1359765,0.002977695,0.0002316475,0.0000530953,0.0001313953,0.00001771625,0.000267309],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8553251,"threshold_uncertainty_score":0.3810517,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058753079","doi":"10.1118/1.4800806","title":"Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls","year":2013,"lang":"en","type":"review","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":353,"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":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Computer science; Context (archaeology); Positron emission tomography; Image resolution; Point spread function; Observer (physics); Resolution (logic); Medical imaging; Medical physics; Artificial intelligence; Nuclear medicine; Medicine; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.07700151388050115,"gpt":0.3870551149579711,"spread":0.3100536010774699,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001117746,0.0003403237,0.001197839,0.0001023262,0.00006020656,0.00003605938,0.0002327935,0.0002118174,0.0001108977],"category_scores_gemma":[0.001535,0.000252601,0.0001778372,0.0003272593,0.0002177528,0.0001295561,0.0002068174,0.001422999,0.00007552913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097267,"about_ca_system_score_gemma":0.0004270361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008125707,"about_ca_topic_score_gemma":8.508208e-7,"domain_scores_codex":[0.9975281,0.0001620485,0.0006902865,0.000519316,0.0007369362,0.0003633572],"domain_scores_gemma":[0.9981765,0.0005150724,0.0002068704,0.0005255108,0.0001077619,0.0004682639],"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":[0.000003647265,0.0002051734,0.000002172754,0.002604658,0.00002909832,0.00004978041,0.00001778379,3.669016e-7,1.374926e-7,0.00958076,0.004400757,0.9831057],"study_design_scores_gemma":[0.0003406985,0.00002861783,4.590388e-7,0.019996,0.0006543699,0.0004328145,0.00001436567,0.02043584,5.395466e-7,0.00608835,0.9517225,0.0002854426],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000004986041,0.9631405,0.02988228,0.00387956,0.00006233837,0.001090763,0.00001030299,0.0001457325,0.001783532],"genre_scores_gemma":[0.00008204279,0.9919312,0.005509016,0.001192913,0.0005651264,0.0003605417,0.0001927416,0.00006604208,0.0001003643],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9828202,"threshold_uncertainty_score":0.9999926,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2175610922","doi":"10.2967/jnumed.115.159012","title":"MIRD Pamphlet No. 26: Joint EANM/MIRD Guidelines for Quantitative <sup>177</sup>Lu SPECT Applied for Dosimetry of Radiopharmaceutical Therapy","year":2015,"lang":"en","type":"article","venue":"Journal of Nuclear Medicine","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":350,"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":"Vetenskapsrådet; Gunnar Nilssons Cancerstiftelse","keywords":"Nuclear medicine; Dosimetry; Medicine; Radionuclide therapy; Medical physics; Spect imaging; Iodine-123","retraction":null,"screen_n_in":null,"score":{"opus":0.3052255694822652,"gpt":0.4628987343308874,"spread":0.1576731648486222,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002321134,0.0002579415,0.001120112,0.0002819872,0.00006496489,0.00001074487,0.0002793694,0.0001240967,0.0002026144],"category_scores_gemma":[0.003536783,0.0001667648,0.0003090263,0.0002716928,0.0004187014,0.00007315138,0.00003805147,0.0004324102,0.0000157584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000102095,"about_ca_system_score_gemma":0.0002068891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001290925,"about_ca_topic_score_gemma":1.623359e-7,"domain_scores_codex":[0.9971458,0.00004755313,0.001457573,0.0002510777,0.0007709695,0.000327072],"domain_scores_gemma":[0.9957907,0.0004487199,0.0007096247,0.0003411619,0.002077291,0.0006325661],"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":[0.003155297,0.000444873,0.00006065336,0.0002296168,0.0002761076,0.00001945553,0.0007084353,0.00001185157,0.09839401,0.002913811,0.8855223,0.008263614],"study_design_scores_gemma":[0.02091577,0.008235342,0.0001982063,0.001170707,0.0007166383,0.000393585,0.002845675,0.01989962,0.01471686,0.003508908,0.9270919,0.0003068116],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.340709,0.006992898,0.1404956,0.4977646,0.001352429,0.007535303,0.0001136414,0.0004821991,0.004554404],"genre_scores_gemma":[0.4130597,0.002231635,0.5530583,0.02566505,0.005354187,0.00007819047,0.00003179965,0.0002312609,0.0002899053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4720995,"threshold_uncertainty_score":0.680047,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2625583840","doi":"10.1007/s00259-017-3740-2","title":"EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies","year":2017,"lang":"en","type":"review","venue":"European Journal of Nuclear Medicine and Molecular Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":349,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Accreditation; Medicine; Harmonization; Medical physics; Positron emission tomography; Nuclear medicine; Clinical Practice; Family medicine; Medical education","retraction":null,"screen_n_in":null,"score":{"opus":0.1804640995701383,"gpt":0.4620990863683356,"spread":0.2816349867981973,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001595738,0.0002941295,0.001323851,0.0003101173,0.0001161711,0.0001044808,0.0003405787,0.00003595153,0.00002861213],"category_scores_gemma":[0.003704648,0.0002023812,0.0001483458,0.0001825941,0.0002640493,0.0002034205,0.0001699302,0.0008232738,0.00002798519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008844289,"about_ca_system_score_gemma":0.0001569032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001819505,"about_ca_topic_score_gemma":4.75146e-7,"domain_scores_codex":[0.997393,0.0005715098,0.001065504,0.0003580618,0.0004069045,0.0002049843],"domain_scores_gemma":[0.9974922,0.0002716023,0.001049197,0.0004583031,0.0004247614,0.0003039364],"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":[0.00006961955,0.0001922893,0.000008201386,0.001657742,0.00029996,0.01167871,0.001841761,7.864904e-7,0.000677832,0.0002940026,0.08053416,0.9027449],"study_design_scores_gemma":[0.0007541817,0.0002141893,0.00003661367,0.02132414,0.001338232,0.001234974,0.003212364,0.00005755337,0.000001843636,0.00002270949,0.9716387,0.0001645142],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00007187768,0.9334506,0.002486651,0.06016313,0.0001743077,0.0005576756,0.000004646284,0.00004036958,0.003050765],"genre_scores_gemma":[0.001246024,0.9582412,0.03496158,0.004972325,0.0004546965,0.000003908913,0.00002706529,0.00007260498,0.00002062355],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9025804,"threshold_uncertainty_score":0.8252866,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2748739903","doi":"10.1007/s10278-018-0056-0","title":"Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network","year":2018,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":335,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Noise reduction; Generative adversarial network; Artificial intelligence; Noise (video); Process (computing); Reduction (mathematics); Deep learning; Pattern recognition (psychology); Image (mathematics); Computer vision; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03182686661431799,"gpt":0.3407278642159383,"spread":0.3089009976016203,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002337668,0.0001486082,0.0003011884,0.0001149838,0.0001834407,0.0001800945,0.0001362893,0.00002446742,0.0001457261],"category_scores_gemma":[0.0001629753,0.0001234347,0.0001657179,0.0002278127,0.0002904707,0.0007237362,0.00007106286,0.0003109707,0.00001567115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001292474,"about_ca_system_score_gemma":0.0002652041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003968183,"about_ca_topic_score_gemma":2.393796e-7,"domain_scores_codex":[0.998615,0.00001851829,0.0005141741,0.0001616809,0.0004150479,0.0002755978],"domain_scores_gemma":[0.9986351,0.00006908138,0.0003561668,0.0001519737,0.0005371828,0.0002505025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001736957,0.002924147,0.2092379,0.0004507378,0.001754122,0.006721266,0.001868569,0.001414509,0.2159542,0.007806911,0.4282753,0.1218554],"study_design_scores_gemma":[0.02456452,0.00225542,0.02360291,0.01195152,0.002829573,0.07413034,0.001948905,0.4850141,0.09322619,0.09674037,0.1803703,0.00336587],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6072462,0.0002121673,0.3843581,0.004918267,0.0006113095,0.0002781535,0.00003977103,0.00008906283,0.002246998],"genre_scores_gemma":[0.9726999,0.000006288544,0.02196643,0.001081107,0.004113472,0.000001706127,0.00002966294,0.00002623967,0.00007516902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4835996,"threshold_uncertainty_score":0.5033522,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1978760292","doi":"10.1016/s0167-7799(02)02004-8","title":"Micro-CT in small animal and specimen imaging","year":2002,"lang":"en","type":"article","venue":"Trends in biotechnology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":333,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Robarts Clinical Trials","funders":"Canadian Arthritis Network","keywords":"Computer science; Computed tomography; Biomedical engineering; Tomography; High resolution; Medical physics; Radiology; Medicine; Remote sensing","retraction":null,"screen_n_in":null,"score":{"opus":0.03439507568646941,"gpt":0.3045541548149636,"spread":0.2701590791284941,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000993147,0.0001010925,0.0002068922,0.0005905334,0.00001785276,0.000005602465,0.0001043511,0.00009925674,0.0003241621],"category_scores_gemma":[0.00003244178,0.00009383027,0.00002128534,0.0005340612,0.0002437385,0.00001849769,0.00008755536,0.0004255532,0.00001752458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004876115,"about_ca_system_score_gemma":0.00000387184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009704881,"about_ca_topic_score_gemma":0.00004480342,"domain_scores_codex":[0.9992099,0.00001083127,0.0002077367,0.0002788686,0.00004284957,0.0002498331],"domain_scores_gemma":[0.9996228,0.00001923376,0.00003097058,0.000270668,0.000006050117,0.00005022647],"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":[0.00002551437,0.0004538561,0.08194105,0.00003026813,0.000009900688,0.0006173778,0.0001254504,8.301339e-8,0.2236989,0.0122635,0.01203306,0.668801],"study_design_scores_gemma":[0.008927295,0.0007188652,0.2278274,0.0005728001,0.00009257256,0.004426391,0.0003540436,0.02504859,0.2967738,0.007240889,0.4269283,0.0010891],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.886847,0.0007635921,0.0001170773,0.1059657,0.00001590134,0.000115399,0.000002546604,0.0002688288,0.005903946],"genre_scores_gemma":[0.9759593,0.0004277308,0.02252442,0.0005107213,0.00002383955,0.00002679775,0.000005887649,0.00001272416,0.000508555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6677119,"threshold_uncertainty_score":0.3826287,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2007486866","doi":"10.1016/s0360-3016(01)01722-9","title":"Observer variation in contouring gross tumor volume in patients with poorly defined non-small-cell lung tumors on CT: the impact of 18 FDG-hybrid PET fusion","year":2001,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":318,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Contouring; Medicine; Nuclear medicine; Coefficient of variation; Positron emission tomography; Fiducial marker; Radiation therapy; Radiology; Lung cancer; Pathology","retraction":null,"screen_n_in":null,"score":{"opus":0.01502940160310489,"gpt":0.3087125184655292,"spread":0.2936831168624243,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006202741,0.0001825808,0.0004344164,0.0002240114,0.00004434119,0.00001722762,0.0003336662,0.0000517555,0.00008091643],"category_scores_gemma":[0.0002267572,0.0001238897,0.0001536667,0.0002729108,0.0001078514,0.0001616733,0.00004803075,0.000558838,0.000006238308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008181118,"about_ca_system_score_gemma":0.0004254431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003455076,"about_ca_topic_score_gemma":0.00001181303,"domain_scores_codex":[0.9983193,0.0001454958,0.0008029045,0.0002033812,0.0003084911,0.0002204365],"domain_scores_gemma":[0.9978685,0.0002807794,0.001055995,0.000183902,0.0005138185,0.00009702536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007355678,0.001193447,0.988613,0.000009420947,0.00008839274,0.00008674846,0.0001854615,0.001403901,0.002105667,0.0004217548,0.0008876425,0.004269009],"study_design_scores_gemma":[0.007155996,0.001333,0.973837,0.0002201715,0.00004938261,0.0001196677,0.00002367366,0.01514707,0.0006473351,0.001064577,0.0002814159,0.0001206895],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920096,0.00002594008,0.005300065,0.001585871,0.0001954012,0.0005021376,0.00003151441,0.00001697715,0.0003324685],"genre_scores_gemma":[0.9968365,0.00006545022,0.002134589,0.0004491763,0.0003462533,0.00002466531,0.000090032,0.00002205399,0.00003124891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01477596,"threshold_uncertainty_score":0.5052077,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2024940817","doi":"10.1176/appi.ajgp.12.6.584","title":"In-Vivo Imaging of Alzheimer Disease  -Amyloid With [11C]SB-13 PET","year":2004,"lang":"en","type":"article","venue":"American Journal of Geriatric Psychiatry","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":316,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre for Addiction and Mental Health","funders":"","keywords":"Positron emission tomography; In vivo; Binding potential; Nuclear medicine; Pittsburgh compound B; Alzheimer's disease; Medicine; Cognitive impairment; Pet imaging; Chemistry; Pathology; Disease; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.006411174321907099,"gpt":0.2814551320049152,"spread":0.2750439576830082,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002642336,0.0001713245,0.0005264286,0.0003634827,0.00003527,0.000009966721,0.0002004334,0.00001541824,0.00012011],"category_scores_gemma":[0.00004029512,0.0001266644,0.000176453,0.0008786642,0.0003352804,0.000128991,0.0000268478,0.0003628585,0.000003658917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005220328,"about_ca_system_score_gemma":0.0008456596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001338507,"about_ca_topic_score_gemma":0.000002716876,"domain_scores_codex":[0.9983972,0.00003296138,0.0006863406,0.0001911117,0.0004333871,0.0002589581],"domain_scores_gemma":[0.9982684,0.00002959713,0.000762373,0.0003416326,0.0001669662,0.0004310455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003696668,0.00548307,0.8991042,0.0003605541,0.0007631509,0.001533436,0.0008897636,0.0002983343,0.008415364,0.00573788,0.05741693,0.01630061],"study_design_scores_gemma":[0.03887057,0.01093737,0.8135865,0.006919204,0.008393734,0.02092837,0.006283697,0.0006494286,0.006635464,0.03339342,0.05046957,0.00293269],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9402936,0.001756065,0.006312483,0.05002217,0.0002989686,0.0002980462,0.00001208578,0.00003709024,0.0009695246],"genre_scores_gemma":[0.9095926,0.0002852254,0.08806416,0.001588277,0.000409054,0.000008818431,0.000001573688,0.00002902809,0.0000212045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08551776,"threshold_uncertainty_score":0.5165223,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137969878","doi":"10.1109/tmi.2004.832656","title":"A Method for Modeling Noise in Medical Images","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":308,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Gaussian noise; Noise (video); Shot noise; Image noise; Computer science; Artificial intelligence; Quantization (signal processing); Medical imaging; Computer vision; Pattern recognition (psychology); Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.02836811517753919,"gpt":0.3791660219313389,"spread":0.3507979067537997,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001283483,0.0002337131,0.0004333483,0.0003236459,0.0001483977,0.00002498399,0.0002693189,0.0002067923,0.000582148],"category_scores_gemma":[0.0002889219,0.0002028608,0.0002277676,0.0004270768,0.0001880017,0.0001125935,0.000004114201,0.001089368,0.0000297641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001805557,"about_ca_system_score_gemma":0.000605394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003203571,"about_ca_topic_score_gemma":0.00003015181,"domain_scores_codex":[0.9971309,0.00005105866,0.0006167915,0.0005257417,0.001163702,0.0005118262],"domain_scores_gemma":[0.9983034,0.0003100508,0.00004699365,0.0003715354,0.0001102355,0.0008578244],"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":[0.0003838434,0.003615674,0.00009195552,0.0004512329,0.0001149521,0.0007747482,0.000506054,0.007958914,0.0130608,0.001744188,0.003165559,0.9681321],"study_design_scores_gemma":[0.004988086,0.00008724496,0.00001558688,0.001298738,0.0001115602,0.0005823035,0.000128763,0.9713154,0.0156042,0.003383161,0.002198981,0.0002859904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002808396,0.00007254415,0.9050611,0.09049619,0.0001585523,0.000647706,0.00001211393,0.0003296392,0.000413738],"genre_scores_gemma":[0.697288,0.0002228428,0.2914884,0.01001893,0.0001985136,0.0006073292,0.00001189569,0.00005825318,0.0001058967],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9678461,"threshold_uncertainty_score":0.8272422,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2164547129","doi":"10.1016/j.ejca.2008.10.028","title":"Evaluation of lymph nodes with RECIST 1.1","year":2008,"lang":"en","type":"article","venue":"European Journal of Cancer","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":297,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Lymph; Medicine; Lymph node; Malignancy; Radiology; Pathology","retraction":null,"screen_n_in":null,"score":{"opus":0.0825629448717539,"gpt":0.3670388252229543,"spread":0.2844758803512004,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001222149,0.00004405033,0.0001324119,0.0000468099,0.00002473288,0.000002190681,0.00006869961,0.000006427519,0.000229011],"category_scores_gemma":[0.0001197891,0.00002797552,0.0000399262,0.00009651606,0.00009785857,0.00003282928,0.000007803515,0.00008975679,0.000003649172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000502613,"about_ca_system_score_gemma":0.0002684401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008280985,"about_ca_topic_score_gemma":3.68074e-7,"domain_scores_codex":[0.9988956,0.0001030292,0.0002459032,0.00005015387,0.0006501686,0.00005514901],"domain_scores_gemma":[0.9986244,0.00001415737,0.0002384585,0.0001147645,0.0009298291,0.00007838439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004650317,0.0004431392,0.04819816,0.00007905223,0.0003626626,0.0003445301,0.000892386,0.0002536213,0.04712478,0.0001817108,0.08133242,0.8203225],"study_design_scores_gemma":[0.01060754,0.001056077,0.7065259,0.002727385,0.002039126,0.003616113,0.000146183,0.002912845,0.0468651,0.0002199748,0.2229934,0.0002903068],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9425802,0.001887556,0.009116873,0.00555665,0.00006721742,0.0001347679,0.000002814458,0.0000175625,0.04063638],"genre_scores_gemma":[0.9865546,0.0005188198,0.01202341,0.00024246,0.0002227848,0.000001967247,6.070122e-7,0.0000124795,0.0004228485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8200322,"threshold_uncertainty_score":0.250751,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2892892233","doi":"10.1007/s00259-018-4153-6","title":"Dynamic whole-body PET imaging: principles, potentials and applications","year":2018,"lang":"en","type":"review","venue":"European Journal of Nuclear Medicine and Molecular Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":283,"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":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Siemens USA","keywords":"Positron emission tomography; Magnetic resonance imaging; Nuclear medicine; Standardized uptake value; Whole body imaging; Computer science; Dynamic imaging; Medical physics; Medicine; Artificial intelligence; Radiology; Image processing; Image (mathematics); Digital image processing","retraction":null,"screen_n_in":null,"score":{"opus":0.03141997357281792,"gpt":0.3397186106925305,"spread":0.3082986371197125,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001432144,0.0004190813,0.001353232,0.0004710824,0.0001590127,0.00007237778,0.0003342698,0.00003321042,0.0000511398],"category_scores_gemma":[0.0002372812,0.0003033916,0.0002735144,0.0002550356,0.0007346926,0.00008628721,0.000264743,0.0008222327,0.00003071735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000534465,"about_ca_system_score_gemma":0.0001194812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002473184,"about_ca_topic_score_gemma":3.64549e-8,"domain_scores_codex":[0.997299,0.0002929887,0.001221979,0.0004504399,0.0004312104,0.0003044361],"domain_scores_gemma":[0.9975964,0.00006546669,0.0009508306,0.0005423087,0.0002774165,0.000567529],"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":[0.00001419793,0.0001118223,0.000007867568,0.004905154,0.0002733607,0.003571675,0.0001247554,2.65088e-8,0.001579887,0.0006598973,0.06333799,0.9254134],"study_design_scores_gemma":[0.0006568925,0.0001392433,0.00001516506,0.01394915,0.002274283,0.01658497,0.000094975,0.0001814173,0.000001528164,0.00005226044,0.9658173,0.000232779],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001818685,0.9695327,0.01230189,0.01350546,0.00008637578,0.0006821814,0.00001102936,0.00008124424,0.003780927],"genre_scores_gemma":[0.0004014716,0.9804353,0.0144213,0.003793579,0.0005963122,0.00000826425,0.00005472767,0.0001878884,0.0001012269],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9251806,"threshold_uncertainty_score":0.9999418,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2015583343","doi":"10.1016/j.neuroimage.2006.03.052","title":"A new improved version of the realistic digital brain phantom","year":2006,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":283,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Imaging phantom; Computer science; Voxel; Ground truth; Segmentation; Computer vision; Artificial intelligence; Process (computing); Medical physics; Biomedical engineering; Nuclear medicine; Physics; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.01122548675510944,"gpt":0.2761005889552632,"spread":0.2648751022001538,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003978358,0.00005848629,0.00009250901,0.00002038983,0.00002950002,0.00001308774,0.0001055494,0.00002468474,0.0000610546],"category_scores_gemma":[0.0001632777,0.00003837212,0.00006387906,0.0001344665,0.00006780959,0.00003509257,0.00005415054,0.0001093432,0.00001052415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001055294,"about_ca_system_score_gemma":0.00005088292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002369089,"about_ca_topic_score_gemma":0.000001930211,"domain_scores_codex":[0.9994893,0.000008482233,0.0001349979,0.000125372,0.0001446027,0.00009722362],"domain_scores_gemma":[0.9994823,0.00005328071,0.0000520901,0.0003256066,0.00002770001,0.00005897302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002164595,0.0000933614,0.001378971,0.00002861521,0.000002710656,0.000007757115,0.00000934932,1.962685e-7,0.5032934,0.001071493,0.4874887,0.006603816],"study_design_scores_gemma":[0.003674177,0.0004426008,0.1508983,0.000288239,0.0001853335,0.0002149455,0.00001922883,0.007231717,0.1556098,0.007991897,0.6731356,0.0003081966],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6982152,0.00008734166,0.0509936,0.1410132,0.0002723762,0.00181893,0.0001709866,0.000661336,0.106767],"genre_scores_gemma":[0.9892592,0.000002524289,0.002813383,0.001017518,0.0001174748,0.000004567939,0.00002382882,0.00001248929,0.006749029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3476836,"threshold_uncertainty_score":0.156477,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2775442832","doi":"10.1016/j.neuron.2018.08.039","title":"An Open Resource for Non-human Primate Imaging","year":2018,"lang":"en","type":"article","venue":"Neuron","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":282,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Western University","funders":"National Eye Institute; National Institute on Aging; Medical Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Fondation de France; Science and Technology Commission of Shanghai Municipality; Directorate for Biological Sciences; Shanghai Municipal Education Commission; John Templeton Foundation; National Natural Science Foundation of China; National Institute of Neurological Disorders and Stroke; National Centre for the Replacement, Refinement and Reduction of Animals in Research; Fondation Brain Canada; European Commission; Natural Science Foundation of Shanghai; Newcastle University; University of Oxford; McKnight Foundation; Fondation Neurodis; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Agence Nationale de la Recherche; Biotechnology and Biological Sciences Research Council; Wellcome Trust; Child Mind Institute; Icahn School of Medicine at Mount Sinai; New York Stem Cell Foundation; Max-Planck-Gesellschaft; Royal Society; University of Minnesota; National Science Foundation; National Institute of Mental Health; Fondation pour la Recherche Médicale; McGill University; Institut National de la Santé et de la Recherche Médicale; BRAIN Initiative","keywords":"Non human primate; Neuroimaging; Primate; Data science; Computer science; Data sharing; Neuroscience; Psychology; Medicine; Biology; Evolutionary biology; Pathology","retraction":null,"screen_n_in":null,"score":{"opus":0.0376705750661239,"gpt":0.4243667804868751,"spread":0.3866962054207512,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001610699,0.00006575435,0.0001096687,0.00002891918,0.0001458753,0.00004792528,0.0002576458,0.00001994531,0.0000806652],"category_scores_gemma":[0.00003263412,0.00005641228,0.00002074309,0.00006009637,0.00008998601,0.00006818757,0.00008756427,0.00008368518,0.00002126965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001297811,"about_ca_system_score_gemma":0.00002097143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003106403,"about_ca_topic_score_gemma":0.000001408577,"domain_scores_codex":[0.9993953,0.0000103684,0.0001196596,0.0002297046,0.0000871695,0.0001578383],"domain_scores_gemma":[0.9993234,0.00001680784,0.00003739224,0.0004416512,0.00005138263,0.000129322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006586533,0.0002643959,0.001849058,0.00005791924,0.000004996187,0.000007525804,0.000137122,5.540251e-8,0.6071779,0.002825953,0.3592521,0.02835709],"study_design_scores_gemma":[0.0008122282,0.0006481027,0.0047495,0.00006795123,0.00003732523,0.0000256843,0.00001539853,0.006670204,0.05366737,0.001023998,0.9321791,0.0001031499],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7846555,0.00001473216,0.05198273,0.05345716,0.0001481637,0.004602564,0.00002511355,0.0009432069,0.1041709],"genre_scores_gemma":[0.9536335,0.000001733287,0.03595858,0.008373888,0.0004766326,0.0001709837,0.00005532384,0.00003706673,0.001292219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.572927,"threshold_uncertainty_score":0.2300426,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2001374473","doi":"10.1118/1.2148916","title":"A simple, direct method for x‐ray scatter estimation and correction in digital radiography and cone‐beam CT","year":2005,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":281,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; Ontario Institute for Cancer Research","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; National Institutes of Health","keywords":"Collimator; Imaging phantom; Image quality; Cone beam computed tomography; Optics; Detector; Collimated light; Projection (relational algebra); Nuclear medicine; Artifact (error); Radiography; Pixel; Iterative reconstruction; Physics; Mathematics; Computer science; Computer vision; Medicine; Image (mathematics); Algorithm; Computed tomography; Radiology","retraction":null,"screen_n_in":null,"score":{"opus":0.01458758827161879,"gpt":0.3352466543271577,"spread":0.3206590660555389,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002946137,0.0001025394,0.0002382864,0.0000625814,0.00004635473,0.00002543151,0.00003886987,0.00005130309,0.00002135309],"category_scores_gemma":[0.0003391265,0.0000847902,0.00004891113,0.0001942173,0.0001525421,0.000111966,0.00002589849,0.0001859802,0.0000021321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002211348,"about_ca_system_score_gemma":0.00003211365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002378867,"about_ca_topic_score_gemma":0.000002841314,"domain_scores_codex":[0.9991719,0.00001083898,0.0002100639,0.0002281261,0.000222418,0.0001566987],"domain_scores_gemma":[0.9992915,0.0002962686,0.000044047,0.0001238466,0.00003289037,0.0002114502],"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":[0.00002778283,0.0002183502,0.0065724,0.0000759845,0.00002121804,0.000003498247,0.00007888495,0.000006829829,0.00027186,0.000176852,0.02557592,0.9669704],"study_design_scores_gemma":[0.003486942,0.0002927144,0.009887499,0.0003871699,0.0001611404,0.0001642721,0.00003239373,0.9016152,0.007784673,0.0102705,0.06557184,0.0003456491],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1626198,0.0001341098,0.8255917,0.009931426,0.00006172409,0.0007650506,0.00001585509,0.0001564229,0.0007238822],"genre_scores_gemma":[0.9736296,0.00006655498,0.02386449,0.001859713,0.000267562,0.0001429039,0.00008786807,0.0000158901,0.00006540488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9666247,"threshold_uncertainty_score":0.3457644,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2134495751","doi":"10.1109/tmi.2006.883453","title":"Twenty New Digital Brain Phantoms for Creation of Validation Image Data Bases","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":264,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; McGill Genome Centre; Montreal Neurological Institute and Hospital","funders":"","keywords":"Voxel; Imaging phantom; Computer science; Grey matter; Segmentation; Magnetic resonance imaging; Partial volume; White matter; Artificial intelligence; Image segmentation; Medical imaging; Image processing; Computer vision; Nuclear medicine; Medicine; Radiology; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.03052577536095588,"gpt":0.3531040945246448,"spread":0.3225783191636889,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003803219,0.0001572184,0.0002656596,0.0001587509,0.0001044602,0.00005000704,0.0002628358,0.00007470974,0.000456171],"category_scores_gemma":[0.0002666524,0.0001393303,0.0001172379,0.0002630993,0.0002300792,0.0004053977,0.000005103459,0.000238615,0.00001498882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004672157,"about_ca_system_score_gemma":0.0002362256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000246616,"about_ca_topic_score_gemma":0.00000623398,"domain_scores_codex":[0.9982113,0.00002278609,0.0004979805,0.0003920338,0.0006371131,0.0002387919],"domain_scores_gemma":[0.9984563,0.0004229868,0.0001165782,0.0006016899,0.0001251788,0.0002772414],"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":[0.0001913398,0.001671853,0.0003560862,0.0002583922,0.00007449934,0.00001950329,0.00005640346,0.00004011918,0.05425709,0.0004937155,0.2532599,0.6893211],"study_design_scores_gemma":[0.007619905,0.0003137288,0.0004829503,0.001454726,0.0007487678,0.0002816439,0.0001703851,0.4108431,0.4280211,0.004094238,0.1452846,0.0006847752],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004236873,0.00003162666,0.9604645,0.03313125,0.0001284648,0.0005780386,0.0002394754,0.0002307077,0.0009590229],"genre_scores_gemma":[0.9496524,0.00003936339,0.04595317,0.00128498,0.0004034862,0.00008325579,0.001008064,0.00004461464,0.001530641],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9454156,"threshold_uncertainty_score":0.5681726,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3027275779","doi":"10.1088/1361-6560/ab9500","title":"Roadmap toward the 10 ps time-of-flight PET challenge","year":2020,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":259,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"National Cancer Institute; National Institutes of Health; Agence Nationale de la Recherche","keywords":"Time of flight; Computer science; Medical physics; Aeronautics; Physics; Optics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.2889427721695977,"gpt":0.4254892078516912,"spread":0.1365464356820935,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001668141,0.00008578106,0.0003066839,0.00001902704,0.00001971447,0.000001018874,0.0001066639,0.00003917405,0.0002223555],"category_scores_gemma":[0.0001422562,0.00004530693,0.00002821607,0.0001579812,0.0003999967,0.00001067695,0.00006064067,0.0002147132,0.00001686554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004887655,"about_ca_system_score_gemma":0.00002304062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000260359,"about_ca_topic_score_gemma":3.846963e-7,"domain_scores_codex":[0.9993994,0.00003414842,0.0002012878,0.0001729171,0.00006923622,0.0001229822],"domain_scores_gemma":[0.9995249,0.0001286627,0.00005661757,0.0001620256,0.00003894349,0.00008885407],"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":[0.0004544422,0.001040429,0.01314908,0.001079853,0.0002790141,0.00008611514,0.01621806,0.000001569196,0.2683401,0.1636282,0.3479826,0.1877406],"study_design_scores_gemma":[0.00700732,0.005997802,0.004658029,0.0008772691,0.0005015429,0.0001335085,0.002310752,0.01936363,0.01097263,0.09428836,0.8532581,0.0006310882],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1080336,0.00171893,0.004716596,0.8703106,0.00006123592,0.0005967242,0.000009676691,0.0001001977,0.01445244],"genre_scores_gemma":[0.9903977,0.0009765693,0.001114891,0.00671097,0.0006470014,0.00002813649,0.00003830516,0.000007728244,0.00007873442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8823641,"threshold_uncertainty_score":0.2434637,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2144240099","doi":"10.2967/jnumed.111.099382","title":"NEMA NU 4-2008 Comparison of Preclinical PET Imaging Systems","year":2012,"lang":"en","type":"article","venue":"Journal of Nuclear Medicine","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":242,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Winnipeg; University of British Columbia; University of Alberta; Université de Sherbrooke; University of Manitoba","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Pet imaging; Computer science; Nuclear medicine; Medical physics; Positron emission tomography; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.07824379849892571,"gpt":0.4274150910693106,"spread":0.3491712925703849,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00132372,0.0001210076,0.0007892707,0.0001638539,0.00004222526,0.000005223543,0.0001803285,0.00004959023,0.00042394],"category_scores_gemma":[0.000778591,0.00007925479,0.0001327828,0.0001858325,0.0002990726,0.0001097947,0.00004133526,0.000554837,0.00001809335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000479378,"about_ca_system_score_gemma":0.0000542841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002512373,"about_ca_topic_score_gemma":9.504211e-8,"domain_scores_codex":[0.9977741,0.00006568067,0.001188357,0.00009275811,0.0006420485,0.0002370038],"domain_scores_gemma":[0.9979331,0.0002083056,0.0007691343,0.0002842295,0.0003146597,0.0004904919],"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":[0.0002353235,0.001230559,0.08158173,0.0003881405,0.0001421113,0.00006910296,0.001326061,0.000002730874,0.03034239,0.002354588,0.8751692,0.007158027],"study_design_scores_gemma":[0.00434979,0.001723984,0.04270033,0.004389902,0.001035932,0.006660422,0.003490008,0.009143381,0.0005464776,0.0001324384,0.9255974,0.0002299295],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8356068,0.01356903,0.005995503,0.1258559,0.002449617,0.0008746409,0.000006617445,0.0001752945,0.01546658],"genre_scores_gemma":[0.988333,0.0002351022,0.008500392,0.001019069,0.001774015,0.000001607927,0.000002645939,0.00002797404,0.0001061539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1527262,"threshold_uncertainty_score":0.4641846,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2099326137","doi":"10.1016/j.medengphy.2007.11.003","title":"Improved reproducibility of high-resolution peripheral quantitative computed tomography for measurement of bone quality","year":2008,"lang":"en","type":"article","venue":"Medical Engineering & Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":231,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Reproducibility; Quantitative computed tomography; Biomedical engineering; Repeatability; Scanner; Peripheral; Nuclear medicine; Medicine; Materials science; Computer science; Bone density; Mathematics; Osteoporosis; Pathology; Artificial intelligence; Internal medicine; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.05768747993005931,"gpt":0.3208059329661042,"spread":0.2631184530360449,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001576785,0.0001307584,0.0005345055,0.0000427987,0.00003508674,0.0000014172,0.00009859687,0.00008565294,0.00001236844],"category_scores_gemma":[0.002224333,0.0001151941,0.0001952076,0.0003084541,0.0002446713,0.0000313371,0.00003959308,0.0002080031,2.113767e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005745023,"about_ca_system_score_gemma":0.0001525899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001634474,"about_ca_topic_score_gemma":0.000001400615,"domain_scores_codex":[0.9980265,0.00002846445,0.0006270937,0.000408571,0.0007219259,0.000187393],"domain_scores_gemma":[0.99834,0.0001259877,0.0001670096,0.0007001727,0.0004859041,0.0001809403],"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":[0.0006560385,0.004352489,0.002515464,0.004734164,0.0005652937,0.000007982205,0.0009822386,0.001291514,0.9051968,0.05751419,0.004571514,0.01761228],"study_design_scores_gemma":[0.004173648,0.001290436,0.04630248,0.001023172,0.0002158198,0.00002161372,0.00003835031,0.6354468,0.3078194,0.001618897,0.001609223,0.0004401263],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2879373,0.0001310747,0.7102522,0.0009826039,0.00006844039,0.0004824498,0.00001939113,0.0001156874,0.00001078806],"genre_scores_gemma":[0.8630677,0.00001627223,0.1366094,0.00005495625,0.0001170465,0.00007562798,0.0000384241,0.00001529617,0.000005276064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6341553,"threshold_uncertainty_score":0.4697477,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2565897211","doi":"10.1016/j.neuroimage.2016.12.010","title":"A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients","year":2016,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":230,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Lawson Health Research Institute","funders":"National Center for Advancing Translational Sciences; Institute for Mental and Physical Health and Clinical Translation; National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Rigshospitalet; Agence Nationale de la Recherche; University College London; Medical Research Council; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research","keywords":"Cohort; Correction for attenuation; Attenuation; Medicine; Nuclear medicine; Medical physics; Positron emission tomography; Internal medicine; Physics; Optics","retraction":null,"screen_n_in":null,"score":{"opus":0.06463436439049386,"gpt":0.3919226007998171,"spread":0.3272882364093233,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001300585,0.0001100673,0.000263153,0.0001137554,0.00003825094,0.000004593304,0.0000876933,0.00006031779,0.0001287557],"category_scores_gemma":[0.002065443,0.0000830312,0.00009614942,0.0002104646,0.00009022352,0.00009913125,0.00004984456,0.0001174627,0.000005629978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008260609,"about_ca_system_score_gemma":0.0001338571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003295298,"about_ca_topic_score_gemma":0.000004124823,"domain_scores_codex":[0.9981911,0.0001770833,0.0005763254,0.0002856734,0.0006093223,0.0001604719],"domain_scores_gemma":[0.9981026,0.0001569383,0.0003905078,0.0003846028,0.0008840488,0.00008131824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001096307,0.001444853,0.4906492,0.00006916795,0.00002447902,0.000002115245,0.00005699837,7.228345e-7,0.4685173,0.00002589791,0.009297227,0.02980238],"study_design_scores_gemma":[0.006254308,0.0006522107,0.7673321,0.001057478,0.0006397318,0.00002387048,0.00003360312,0.08280911,0.1370784,0.0002208353,0.003625558,0.0002728615],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9195377,0.000006018712,0.07651641,0.001827364,0.00009649566,0.001561117,0.00004893427,0.0001285787,0.0002773334],"genre_scores_gemma":[0.9631447,0.00002682471,0.03615964,0.0002735477,0.00004373523,0.00004544598,0.00005358555,0.00002367003,0.0002288632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.331439,"threshold_uncertainty_score":0.3385914,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1832475542","doi":"","title":"Respiratory gating for 3-dimensional PET of the thorax: feasibility and initial results.","year":2004,"lang":"en","type":"article","venue":"PubMed","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":227,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Hôpital Fleurimont; Centre Hospitalier Universitaire de Sherbrooke","funders":"","keywords":"Nuclear medicine; Coronal plane; Gating; Respiratory system; Imaging phantom; Cardiac PET; Medicine; Amplitude; Thorax (insect anatomy); Image quality; Scanner; Biomedical engineering; Physics; Positron emission tomography; Radiology; Computer science; Internal medicine; Optics; Anatomy; Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.09775891316222107,"gpt":0.3515896469353451,"spread":0.253830733773124,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006136072,0.00004901463,0.0001065253,0.00001646527,0.00006266241,0.000004104112,0.0000545532,0.00002589237,0.00000179737],"category_scores_gemma":[0.00107131,0.00003140332,0.00004393862,0.00007094121,0.0001721547,0.00001873801,0.00004673049,0.00009860274,1.93175e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003500497,"about_ca_system_score_gemma":0.00005623297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001858351,"about_ca_topic_score_gemma":0.000003358898,"domain_scores_codex":[0.9993675,0.00001679835,0.0002063586,0.0001453695,0.0001415677,0.0001224196],"domain_scores_gemma":[0.99944,0.00009489757,0.00007313306,0.000233234,0.00007401036,0.00008478266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005823734,0.004531553,0.101852,0.002710755,0.0003364641,0.00005616109,0.001600186,0.0000698194,0.05683411,0.1019696,0.09763129,0.6265844],"study_design_scores_gemma":[0.006063058,0.0001303084,0.8980368,0.0001634041,0.000117601,0.00006194061,0.00005776632,0.0002079455,0.05835386,0.01756563,0.01908353,0.0001581338],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851958,0.00006037045,0.0003949778,0.01162649,0.00004047174,0.001622216,0.00004638999,0.00004775966,0.0009655606],"genre_scores_gemma":[0.9892812,0.000001711927,0.008777447,0.001203462,0.00008434123,0.000592333,0.000007173057,0.000006049169,0.00004626909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7961848,"threshold_uncertainty_score":0.1282535,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2034946706","doi":"10.1097/01.wcb.0000045042.03034.42","title":"Positron Emission Tomography Compartmental Models: A Basis Pursuit Strategy for Kinetic Modeling","year":2002,"lang":"en","type":"article","venue":"Journal of Cerebral Blood Flow & Metabolism","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":224,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Positron emission tomography; Binding potential; Context (archaeology); Voxel; Parametric statistics; Computer science; Nuclear medicine; Biological system; Artificial intelligence; Mathematics; Medicine; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.05285715286898682,"gpt":0.2967100902845182,"spread":0.2438529374155314,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000345221,0.0002256643,0.0006354185,0.0002525224,0.0001116599,0.00005954662,0.0002547483,0.0001133323,0.0002191716],"category_scores_gemma":[0.00004145826,0.0001700919,0.0005039651,0.0002531535,0.00005307347,0.0002431795,0.00003731689,0.0004129834,0.00000283741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002812279,"about_ca_system_score_gemma":0.00004332126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008618893,"about_ca_topic_score_gemma":3.150136e-7,"domain_scores_codex":[0.998037,0.00004053644,0.000764156,0.0002214864,0.0005876079,0.0003492013],"domain_scores_gemma":[0.9986633,0.00004147075,0.0002745686,0.0002620482,0.0002919521,0.0004666612],"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":[0.00198349,0.01692961,0.001764336,0.001305331,0.004390059,0.0002908226,0.002672137,0.02876056,0.3726104,0.01705401,0.3449478,0.2072915],"study_design_scores_gemma":[0.004913966,0.000554586,0.000122661,0.0002971136,0.001962254,0.0006167978,0.00007776658,0.9646295,0.0125488,0.00735425,0.006682018,0.0002402966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8290465,0.01056804,0.1484168,0.009006055,0.0002973528,0.001112686,0.000055485,0.0001279796,0.001369032],"genre_scores_gemma":[0.8728556,0.0004447541,0.1250459,0.0006660104,0.0006959572,0.00003364412,0.00001584816,0.00003548373,0.0002067952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9358689,"threshold_uncertainty_score":0.6936148,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2106666760","doi":"10.1073/pnas.0915163107","title":"Hybrid PET-optical imaging using targeted probes","year":2010,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":221,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; National Cancer Institute; National Heart, Lung, and Blood Institute; Regione del Veneto; American Heart Association","keywords":"Molecular imaging; In vivo; Preclinical imaging; Molecular probe; Nuclear imaging; Positron emission tomography; Fluorescence; Fluorescence-lifetime imaging microscopy; Tomography; Chemistry; Nuclear medicine; Biophysics; Biomedical engineering; Medicine; Biology; Biochemistry; Optics; Physics; Radiology","retraction":null,"screen_n_in":null,"score":{"opus":0.0400791699500576,"gpt":0.3533375994092481,"spread":0.3132584294591905,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000865908,0.00006738485,0.0001283234,0.0001061257,0.0001426414,0.00001531856,0.0003823587,0.00002495378,0.00003521257],"category_scores_gemma":[0.0008377868,0.00004205629,0.00006276144,0.0003575237,0.001225032,0.0001772801,0.0001060948,0.0003131137,9.061677e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001432151,"about_ca_system_score_gemma":0.00005561751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004240785,"about_ca_topic_score_gemma":6.426941e-9,"domain_scores_codex":[0.9985872,0.000001497037,0.0002517538,0.0001814322,0.0008444368,0.0001336884],"domain_scores_gemma":[0.9993948,0.00005241294,0.0001895921,0.000009878388,0.0002893541,0.00006394526],"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":[0.000003387092,0.00004745716,0.004806845,0.00003643587,0.000003680373,1.569356e-8,0.00001332437,7.569378e-7,0.9428308,0.05050176,0.001414114,0.0003414409],"study_design_scores_gemma":[0.0001305145,0.00001475489,0.01443196,0.0001107245,0.00002537464,0.0001821426,0.00002725162,0.01778721,0.9238813,0.04251396,0.0008350624,0.00005976976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751002,0.00001911593,0.00002752519,0.01962775,0.00001429762,0.0002231651,0.000004188906,0.00003341932,0.004950291],"genre_scores_gemma":[0.9155183,0.000004273639,0.08380092,0.0005099858,0.00009766734,0.000008552773,1.379576e-7,0.000003888592,0.00005625025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0837734,"threshold_uncertainty_score":0.451368,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2075029962","doi":"10.1088/0031-9155/48/8/301","title":"The design and implementation of a motion correction scheme for neurological PET","year":2003,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":210,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre for Addiction and Mental Health","funders":"","keywords":"Imaging phantom; Computer vision; Detector; Computer science; Positron emission tomography; Physics; Tomography; Artificial intelligence; Tracking (education); Data acquisition; Optics; Nuclear medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.2263768113841646,"gpt":0.4622794422121679,"spread":0.2359026308280033,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003948494,0.00004149023,0.0001138442,0.00001675158,0.00004408462,0.000001143154,0.00001623948,0.00002154605,0.000004177061],"category_scores_gemma":[0.0001706166,0.00002300528,0.00000931556,0.00006400787,0.00018676,0.000009002988,0.00000678806,0.0000702457,6.584418e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003466187,"about_ca_system_score_gemma":0.00001105117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001507998,"about_ca_topic_score_gemma":0.000001720105,"domain_scores_codex":[0.9996247,0.00004734003,0.0001292377,0.00009858071,0.00002639132,0.00007370684],"domain_scores_gemma":[0.9995784,0.0002604037,0.00004547077,0.00005763403,0.00003402373,0.00002403401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000239257,0.0002026283,0.07615534,0.0001250265,0.00003973214,0.000001836658,0.0004944345,0.000002155354,0.1470143,0.3172771,0.01205908,0.4463891],"study_design_scores_gemma":[0.01596303,0.01438863,0.0692711,0.0002955233,0.0004418618,0.0004816687,0.003968897,0.1190247,0.0464001,0.6348093,0.09446048,0.0004947091],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4787604,0.000232826,0.5056654,0.01415006,0.0001066835,0.0009313972,0.000001536934,0.00002143192,0.0001302434],"genre_scores_gemma":[0.9855972,0.000492411,0.01331036,0.0004216794,0.00006148976,0.00009263485,0.00001292873,0.000002777187,0.000008538091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5068368,"threshold_uncertainty_score":0.09381282,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1982675592","doi":"10.1118/1.1617353","title":"Fundamental image quality limits for microcomputed tomography in small animals","year":2003,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":208,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"General Electric (Canada); Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research; Lawson Health Research Institute","keywords":"Scanner; Imaging phantom; Voxel; Isotropy; Attenuation coefficient; Image quality; Attenuation; Image resolution; Optics; Iterative reconstruction; Noise (video); Image noise; Physics; Materials science; Computer science; Image (mathematics); Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.05874475636355419,"gpt":0.3715551576740505,"spread":0.3128104013104963,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006214778,0.0001452269,0.0003440574,0.00004868803,0.00005133219,0.00001475097,0.000139637,0.0001163873,0.000117364],"category_scores_gemma":[0.0003796224,0.0001261391,0.0001592945,0.000358324,0.0002065455,0.00002772673,0.00002795373,0.0003173075,0.00001446809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004380083,"about_ca_system_score_gemma":0.0001359459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003113858,"about_ca_topic_score_gemma":0.000007368576,"domain_scores_codex":[0.9986235,0.00005231451,0.0003887897,0.0003108866,0.0003019965,0.0003224756],"domain_scores_gemma":[0.9990137,0.0002151979,0.00006887774,0.0002693248,0.00007623424,0.0003566303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004525571,0.01124089,0.09346902,0.002163601,0.0003018701,0.0001684331,0.0008026619,5.326041e-7,0.2438752,0.273035,0.1235841,0.2509062],"study_design_scores_gemma":[0.01864179,0.001396413,0.04945207,0.001573863,0.0003015798,0.0001454042,0.000260692,0.003461126,0.4890065,0.1607136,0.2733995,0.001647439],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4433244,0.0001702901,0.5412686,0.009909029,0.0001075392,0.001575827,0.00003391461,0.0002941012,0.003316327],"genre_scores_gemma":[0.8970727,0.00003421286,0.09874243,0.003480076,0.0002364675,0.0002410373,0.00007360217,0.00002898321,0.00009053337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4537483,"threshold_uncertainty_score":0.5143805,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1986709694","doi":"10.1038/sj.bjc.6600114","title":"FDG–PET. A possible prognostic factor in head and neck cancer","year":2002,"lang":"en","type":"article","venue":"British Journal of Cancer","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":205,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Thomas Hospital","funders":"","keywords":"Standardized uptake value; Medicine; Positron emission tomography; Head and neck cancer; Stage (stratigraphy); Head and neck squamous-cell carcinoma; Nuclear medicine; Univariate analysis; Cancer; Multivariate analysis; Head and neck; Carcinoma; Oncology; Internal medicine; Surgery; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.03192314064905917,"gpt":0.3414097911688503,"spread":0.3094866505197912,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007620362,0.0000668218,0.000228953,0.00006468537,0.00003209766,0.00003412954,0.00006385312,0.00003308082,0.001176452],"category_scores_gemma":[0.00009227838,0.00006428772,0.00004461088,0.0001526371,0.0000617047,0.00008416759,0.00001562713,0.0003224077,0.000001898884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000735252,"about_ca_system_score_gemma":0.00008025477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000944909,"about_ca_topic_score_gemma":0.0005036963,"domain_scores_codex":[0.9992187,0.00001318106,0.0002914975,0.00010254,0.0002101746,0.0001639091],"domain_scores_gemma":[0.9994946,0.00002578452,0.000113285,0.00006266686,0.0001168947,0.0001867519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003364856,0.0003091911,0.3846779,0.0001491979,0.00004981338,0.001175528,0.0001533973,0.000002779999,0.001670725,0.000030934,0.04795535,0.5637915],"study_design_scores_gemma":[0.003018388,0.0002481474,0.8574033,0.006207495,0.0001042781,0.006224687,0.00002916994,0.001208411,0.0006987065,0.0002835615,0.1243837,0.0001901947],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682918,0.02326617,0.00008927337,0.007605895,0.00006775684,0.0001929524,0.00001795979,0.00001698749,0.000451263],"genre_scores_gemma":[0.9731798,0.02132353,0.003427319,0.0009938681,0.0002656702,0.00004761351,6.597279e-7,0.00001438056,0.0007471769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5636013,"threshold_uncertainty_score":0.9997366,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2582769321","doi":"10.1002/mp.12124","title":"Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211","year":2017,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":204,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Sunnybrook Health Science Centre","funders":"National Cancer Institute; National Institutes of Health","keywords":"Computer science; Segmentation; Algorithm; Artificial intelligence; Image segmentation; Task (project management); Machine learning; Data mining; Medical imaging","retraction":null,"screen_n_in":null,"score":{"opus":0.137341375885745,"gpt":0.4016074920155457,"spread":0.2642661161298008,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009058703,0.00008105458,0.0002282007,0.00002138516,0.00009074447,0.00001597109,0.0001128419,0.00006208181,0.00002730675],"category_scores_gemma":[0.0008136016,0.00006652124,0.00005484822,0.00003949914,0.0004138288,0.000122592,0.000034263,0.0001039893,0.000001111666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002236981,"about_ca_system_score_gemma":0.0001924503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003606574,"about_ca_topic_score_gemma":0.00000178538,"domain_scores_codex":[0.9986982,0.00002100429,0.0003651857,0.0002009947,0.0006218772,0.00009268376],"domain_scores_gemma":[0.9986837,0.00007711752,0.0004376777,0.0004362862,0.0002647499,0.000100492],"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":[0.0002218884,0.002372312,0.01476724,0.003145646,0.0002647204,0.00001543072,0.0007615712,0.000003769242,0.1866699,0.1170695,0.01777337,0.6569347],"study_design_scores_gemma":[0.007793066,0.001308307,0.2539122,0.001451159,0.001547953,0.0001630608,0.001237392,0.5186803,0.06232688,0.1431784,0.007855686,0.0005456019],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4428203,0.00008127096,0.5382722,0.008549311,0.0001550512,0.002290305,0.0000299567,0.00008418818,0.007717452],"genre_scores_gemma":[0.9843813,0.00004369904,0.01476305,0.00004368258,0.0001959913,0.0002268954,0.0002786393,0.00001068648,0.00005606584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6563891,"threshold_uncertainty_score":0.2712657,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3041500444","doi":"10.1016/j.media.2020.101770","title":"Supervised learning with cyclegan for low-dose FDG PET image denoising","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":194,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Siemens (Canada); University Health Network","funders":"National University Cancer Institute, Singapore","keywords":"Artificial intelligence; Noise reduction; Image denoising; Computer science; Pattern recognition (psychology); Computer vision; Supervised learning; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.01569687144382458,"gpt":0.3122459519873317,"spread":0.2965490805435071,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005159642,0.0002390979,0.000726627,0.0001830663,0.000195656,0.00008476505,0.0002833427,0.00009045738,0.002100034],"category_scores_gemma":[0.001922644,0.0001780772,0.0003977794,0.001483833,0.0003423656,0.0001393229,0.00009492382,0.0006290942,0.00006400408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003944257,"about_ca_system_score_gemma":0.0001744472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007507787,"about_ca_topic_score_gemma":0.000009929992,"domain_scores_codex":[0.9975172,0.00006991743,0.0004752213,0.0005868674,0.0009062563,0.0004445893],"domain_scores_gemma":[0.9978591,0.0002212395,0.0001137101,0.0003958447,0.0002604372,0.001149729],"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":[0.002480651,0.003467251,0.05389986,0.00358705,0.0128043,0.008671779,0.004384463,0.0001075537,0.6220152,0.001135281,0.1598252,0.1276214],"study_design_scores_gemma":[0.005659722,0.0008308559,0.002989137,0.0003861604,0.01012502,0.0001682773,0.0008695956,0.9282851,0.01845267,0.0001233587,0.03131316,0.0007969437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1966354,0.00005161424,0.7156268,0.08556194,0.00001090932,0.0005432566,0.00001305153,0.0005097593,0.001047271],"genre_scores_gemma":[0.8072988,0.00007347823,0.1822937,0.00886226,0.0004177228,0.0001516965,0.0004081685,0.00006459629,0.000429582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9281775,"threshold_uncertainty_score":0.9988122,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2564903504","doi":"10.1016/j.neuroimage.2016.12.077","title":"PETPVE12: an SPM toolbox for Partial Volume Effects correction in brain PET – Application to amyloid imaging with AV45-PET","year":2016,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":188,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, San Diego; Biogen Idec; Genentech; National Institutes of Health; Eisai; Servier; Food and Drug Administration; U.S. Department of Defense; Eli Lilly and Company; Lundbeckfonden; Northern California Institute for Research and Education; Alzheimer's Disease Neuroimaging Initiative; GE Healthcare; Alzheimer's Association; Fujirebio US; Pfizer; BioClinica; Roche; University of Southern California; Novartis Pharmaceuticals Corporation; Takeda Pharmaceutical Company; Bristol-Myers Squibb; Alzheimer's Drug Discovery Foundation; Merck; Foundation for the National Institutes of Health","keywords":"Partial volume; Toolbox; Pet imaging; Positron emission tomography; Neuroimaging; Brain size; Volume (thermodynamics); Nuclear medicine; Neuroscience; Computer science; Medicine; Psychology; Physics; Magnetic resonance imaging; Radiology","retraction":null,"screen_n_in":null,"score":{"opus":0.01023323957904187,"gpt":0.2969588875330879,"spread":0.286725647954046,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002919483,0.000174641,0.0002388388,0.0001373531,0.00007728206,0.00003423465,0.0001386946,0.00002423598,0.00003713958],"category_scores_gemma":[0.0003844435,0.0001266546,0.00004943506,0.0002626641,0.00008127952,0.0001847732,0.00003041767,0.0001565224,0.00006074603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007883933,"about_ca_system_score_gemma":0.00005604664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007586253,"about_ca_topic_score_gemma":0.00002922425,"domain_scores_codex":[0.998582,0.00005356907,0.0002485354,0.0005547199,0.000221188,0.0003399721],"domain_scores_gemma":[0.9988266,0.0002000938,0.00007691707,0.0005420444,0.00009199509,0.0002623074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003733207,0.0003643229,0.01366158,0.00009360882,0.000006143937,0.0001001041,0.00007980448,0.000003738233,0.8073713,0.0003179692,0.08935794,0.0882702],"study_design_scores_gemma":[0.009919425,0.003589836,0.19518,0.001056899,0.0002267626,0.001728773,0.00009331956,0.095363,0.1932264,0.0007516113,0.4976341,0.001229887],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4437973,0.000005718433,0.5089273,0.04327205,0.0001640731,0.002746154,0.00002187597,0.0004795838,0.0005859372],"genre_scores_gemma":[0.9758917,0.000003879099,0.01686277,0.004078289,0.0002832726,0.001318256,0.0000504194,0.00005724088,0.001454185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6141448,"threshold_uncertainty_score":0.5164823,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2120892458","doi":"10.1097/00004647-200208000-00014","title":"Positron Emission Tomography Partial Volume Correction: Estimation and Algorithms","year":2002,"lang":"en","type":"article","venue":"Journal of Cerebral Blood Flow & Metabolism","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":180,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Partial volume; Algorithm; Positron emission tomography; Computer science; Noise (video); Tomography; Function (biology); Point spread function; Parametric statistics; Mathematics; Artificial intelligence; Physics; Nuclear medicine; Image (mathematics); Statistics; Optics","retraction":null,"screen_n_in":null,"score":{"opus":0.01165659923165112,"gpt":0.2591772105781038,"spread":0.2475206113464527,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003180855,0.0001445486,0.0004006717,0.0001844796,0.0001078811,0.0000442973,0.00008775158,0.0001032138,0.0003942964],"category_scores_gemma":[0.0001402352,0.0001097035,0.0001777314,0.0002652652,0.0000828588,0.0002198634,0.00002747074,0.0004259372,0.000007868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001180153,"about_ca_system_score_gemma":0.00002522796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008849516,"about_ca_topic_score_gemma":1.371944e-7,"domain_scores_codex":[0.9986719,0.00005078097,0.0005077707,0.0001582041,0.0004208354,0.0001904505],"domain_scores_gemma":[0.998966,0.00003245471,0.0002758929,0.0001752883,0.0001895367,0.0003608119],"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":[0.0001763611,0.001748426,0.01099108,0.0001210232,0.0004701944,0.0001288471,0.000736558,0.00004093298,0.03622457,0.0003382102,0.2589203,0.6901035],"study_design_scores_gemma":[0.007318727,0.001168919,0.03438232,0.0006794761,0.003524308,0.007879065,0.00007564342,0.6810951,0.06359825,0.001307696,0.1984333,0.0005371406],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8715335,0.008268937,0.0884475,0.02768621,0.00198121,0.0006459446,0.00001202676,0.0002076781,0.001217021],"genre_scores_gemma":[0.824392,0.0007391304,0.1712532,0.0006785866,0.001622013,0.00001201701,0.000008570901,0.00002724283,0.001267244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6895664,"threshold_uncertainty_score":0.4473579,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2047359051","doi":"10.1016/j.pscychresns.2006.01.011","title":"An automated method for the extraction of regional data from PET images","year":2006,"lang":"en","type":"article","venue":"Psychiatry Research Neuroimaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":178,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"Mayo Clinic","keywords":"Reproducibility; Positron emission tomography; Intraclass correlation; Artificial intelligence; Computer science; Software; Nuclear medicine; Data set; Brain positron emission tomography; Pattern recognition (psychology); Medicine; Mathematics; Preclinical imaging; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1945754600896958,"gpt":0.5407535825140485,"spread":0.3461781224243526,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001762772,0.0001040656,0.0001740616,0.0001325383,0.0002682744,0.00005924742,0.00062698,0.00002749657,0.0000675802],"category_scores_gemma":[0.0002597453,0.00007382601,0.00006009947,0.0003561024,0.0002359796,0.000191663,0.0001195596,0.0004829631,0.000006274674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001733792,"about_ca_system_score_gemma":0.0001958948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00165818,"about_ca_topic_score_gemma":0.00002387506,"domain_scores_codex":[0.9981288,0.0001995573,0.0003060093,0.0004809424,0.000579842,0.0003048567],"domain_scores_gemma":[0.9969888,0.001093532,0.00009247893,0.001451573,0.0002678903,0.0001056943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001279515,0.0005278612,0.007765549,0.00008407595,0.00002862813,0.000006887744,0.00001283659,0.00001042678,0.2517397,0.002060187,0.7332863,0.004349524],"study_design_scores_gemma":[0.0007674457,0.0001249696,0.06276781,0.0001135525,0.0001065901,0.000141345,0.0001528334,0.8608173,0.002733106,0.01186591,0.06029354,0.0001156301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05272791,0.001300756,0.6173421,0.3219583,0.0004143815,0.002689125,0.0006375206,0.001258297,0.001671574],"genre_scores_gemma":[0.2938234,0.0001107949,0.7030387,0.0007430236,0.001025096,0.0001582473,0.0008516157,0.00004961796,0.0001994701],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8608068,"threshold_uncertainty_score":0.3010538,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1934517153","doi":"10.2967/jnumed.115.156000","title":"Whole-Body PET/MR Imaging: Quantitative Evaluation of a Novel Model-Based MR Attenuation Correction Method Including Bone","year":2015,"lang":"en","type":"article","venue":"Journal of Nuclear Medicine","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":174,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"School of Medicine, New York University; National Institutes of Health; Siemens; National Institute of Biomedical Imaging and Bioengineering; York University; Center for Advanced Imaging Innovation and Research","keywords":"Correction for attenuation; Soft tissue; Nuclear medicine; Whole body imaging; Segmentation; Positron emission tomography; Medicine; Computer science; Radiology; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.2045383251740993,"gpt":0.4633735359298186,"spread":0.2588352107557194,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005984536,0.0001513403,0.0005487562,0.00043713,0.00006307906,0.000008750808,0.0001223029,0.00005199915,0.00007252655],"category_scores_gemma":[0.003718279,0.0001146173,0.0001120766,0.0004236852,0.0001818739,0.0001787846,0.00003091756,0.0004409029,0.000004793163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003175715,"about_ca_system_score_gemma":0.0004629139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004225519,"about_ca_topic_score_gemma":9.2123e-7,"domain_scores_codex":[0.9970425,0.0001576216,0.0008888274,0.000178345,0.001575056,0.0001576973],"domain_scores_gemma":[0.9954303,0.0001998353,0.0009983315,0.0002476891,0.002821127,0.0003027231],"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":[0.0009435271,0.001124639,0.0004166467,0.0001267814,0.0001349978,0.00003439072,0.00256275,0.007172135,0.8085216,0.001684525,0.1613724,0.01590556],"study_design_scores_gemma":[0.005856083,0.0008435568,0.0005111185,0.001082315,0.000853273,0.0005181063,0.001249759,0.982585,0.001669587,0.00070567,0.004030068,0.00009550122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.145273,0.0003525165,0.782272,0.06937494,0.0004933435,0.0005798804,0.000004784092,0.00006538624,0.001584049],"genre_scores_gemma":[0.7856419,0.00001811494,0.212736,0.001218137,0.0002823138,0.00000477709,0.00001724842,0.00003127012,0.00005020004],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9754128,"threshold_uncertainty_score":0.4673958,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2110128011","doi":"10.1016/s0360-3016(02)04609-6","title":"Can PET provide the 3D extent of tumor motion for individualized internal target volumes? A phantom study of the limitations of CT and the promise of PET","year":2003,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":171,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre; Health Sciences Centre","funders":"","keywords":"Imaging phantom; Voxel; Nuclear medicine; Medicine; Positron emission tomography; Volume (thermodynamics); Motion (physics); PET-CT; Tomography; Partial volume; Physics; Optics; Radiology","retraction":null,"screen_n_in":null,"score":{"opus":0.03463598326153995,"gpt":0.3451865943221304,"spread":0.3105506110605905,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001396175,0.0001174432,0.0004649195,0.00009070722,0.00006242804,0.000007078896,0.0004028535,0.00002671387,0.00001152233],"category_scores_gemma":[0.001957782,0.00006293883,0.0001998558,0.0001602348,0.0006007914,0.00006585834,0.00006097522,0.0002705993,1.287291e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000922555,"about_ca_system_score_gemma":0.000411034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007384374,"about_ca_topic_score_gemma":0.00001337268,"domain_scores_codex":[0.9980165,0.0004158998,0.0009972916,0.0001292269,0.0003346841,0.0001063798],"domain_scores_gemma":[0.9954633,0.001046886,0.002229845,0.0002083547,0.001004212,0.00004746731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.004063377,0.01215764,0.6188532,0.000273794,0.005015623,0.00002421819,0.0189568,0.0008686905,0.04632765,0.1772536,0.004561774,0.1116437],"study_design_scores_gemma":[0.1446609,0.01842535,0.2461015,0.00175802,0.006663651,0.004153626,0.01640389,0.0985259,0.1475127,0.2610198,0.05380911,0.0009656207],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782951,0.0001312884,0.01301813,0.00658222,0.0002916849,0.001439348,0.000115676,0.000007065639,0.0001194438],"genre_scores_gemma":[0.9892244,0.0001545053,0.0100734,0.0002585182,0.000137934,0.00009597508,0.00001679734,0.00001188471,0.00002652818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3727517,"threshold_uncertainty_score":0.2566571,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2143992023","doi":"10.1148/rg.295085247","title":"Pediatric FDG PET/CT: Physiologic Uptake, Normal Variants, and Benign Conditions","year":2009,"lang":"en","type":"review","venue":"Radiographics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":171,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Positron emission tomography; Correction for attenuation; Radiology; Nuclear medicine; Gastrointestinal tract; Head and neck; PET-CT; Urinary system; Internal medicine; Surgery","retraction":null,"screen_n_in":null,"score":{"opus":0.04338605551714184,"gpt":0.3487755313533177,"spread":0.3053894758361758,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002813649,0.0004430984,0.001456501,0.0006442582,0.000211116,0.00003960432,0.0002431083,0.0001104136,0.0001064387],"category_scores_gemma":[0.00009372368,0.000343406,0.0006186906,0.001467717,0.0002366773,0.00005642515,0.00005885431,0.001006871,0.00002349954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003003638,"about_ca_system_score_gemma":0.0002177278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001042441,"about_ca_topic_score_gemma":5.573617e-7,"domain_scores_codex":[0.9980268,0.00007872262,0.0006437156,0.0005705079,0.0002851245,0.0003951524],"domain_scores_gemma":[0.9983872,0.0001815749,0.0002922664,0.0006735161,0.00007417282,0.0003912943],"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":[0.000006620427,0.001050733,0.000234834,0.02197112,0.0005543886,0.001688715,0.00001757232,1.463258e-7,0.00000518645,0.0393864,0.2031213,0.731963],"study_design_scores_gemma":[0.0002355107,0.0001403329,0.0005672052,0.00167424,0.003194391,0.003463724,0.000002926631,0.00002950622,1.23304e-7,0.001295268,0.9890805,0.0003162826],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000128753,0.996091,0.0002016155,0.000402095,0.00008365478,0.001214251,0.0001901441,0.0003677001,0.001320793],"genre_scores_gemma":[0.0002457773,0.9904597,0.006741679,0.0004500268,0.0006168926,0.0002358091,0.001022711,0.00004953767,0.0001779082],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7859592,"threshold_uncertainty_score":0.9999018,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2070539368","doi":"10.1201/b13073","title":"Monte Carlo Calculations in Nuclear Medicine","year":2012,"lang":"en","type":"book","venue":"","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":171,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Monte Carlo method; Statistical physics; Computer science; Mathematics; Physics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.03949627402195389,"gpt":0.3321201107852942,"spread":0.2926238367633404,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019969,0.0002053758,0.0004935967,0.0002190871,0.00003636242,0.000004332226,0.0001215082,0.0003100266,0.004858745],"category_scores_gemma":[0.00007590133,0.0001537402,0.00008991676,0.0001135326,0.0002007837,0.00003182092,0.00006067135,0.0006359359,0.0002575017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002142584,"about_ca_system_score_gemma":0.0001454882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003076893,"about_ca_topic_score_gemma":0.00002990848,"domain_scores_codex":[0.9987543,0.00001215684,0.0004092752,0.0002552533,0.000322124,0.0002469199],"domain_scores_gemma":[0.9989442,0.00005795046,0.00008623042,0.0005465619,0.00007088801,0.0002941167],"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":[0.000004815816,0.00005980951,0.0001176204,0.00007823515,0.00002036467,0.00001559991,0.00009438474,2.994606e-7,0.0000585763,0.03620493,0.9615604,0.00178493],"study_design_scores_gemma":[0.0003940796,0.0000429403,0.0004963878,0.00060317,0.0001491555,0.00003984749,0.00001408671,0.0008554621,0.000002508415,0.0009437384,0.9963138,0.0001448405],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0001149896,0.0004645358,0.0002487938,0.02598465,0.0000782274,0.000718641,0.0000090213,0.000321995,0.9720591],"genre_scores_gemma":[0.007736648,0.0002546776,0.006286707,0.00386491,0.0008200768,0.00006040458,0.0001022037,0.00007590614,0.9807985],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.03526119,"threshold_uncertainty_score":0.996051,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3013851350","doi":"10.1186/s40708-020-00104-2","title":"GAN-based synthetic brain PET image generation","year":2020,"lang":"en","type":"article","venue":"Brain Informatics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":171,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; University of Southern California; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; National Institute on Aging; Alzheimer's Association","keywords":"Artificial intelligence; Nuclear medicine; Computer science; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.032133113595816,"gpt":0.3037085090617631,"spread":0.2715753954659471,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002206685,0.0001102086,0.0001707463,0.00004621806,0.00006919059,0.00004216792,0.0001102447,0.0000424374,0.0003040205],"category_scores_gemma":[0.001015356,0.00009458345,0.00006576726,0.0001930054,0.00008405741,0.0000974135,0.0000166319,0.0001883513,0.0002460055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002703703,"about_ca_system_score_gemma":0.0001103188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003685793,"about_ca_topic_score_gemma":2.897284e-7,"domain_scores_codex":[0.9990734,0.0000174096,0.0003975013,0.00008674704,0.0002571735,0.0001677582],"domain_scores_gemma":[0.9991767,0.000124697,0.00009970277,0.0002762917,0.00007291984,0.0002497174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000119798,0.00006148826,0.00004257725,0.0003063374,0.00001222509,0.00001312749,0.0007547851,0.00005756418,0.04302839,0.001370833,0.9497058,0.004634852],"study_design_scores_gemma":[0.0005778933,0.0001164725,0.0000453674,0.00005968921,0.00002872384,0.00003999553,0.0001228023,0.7714707,0.01584898,0.00007652627,0.2114861,0.000126777],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0385209,0.000009514403,0.5484423,0.3975815,0.0000583639,0.0007768308,0.00002838757,0.0005831241,0.013999],"genre_scores_gemma":[0.6034615,0.000006062728,0.2515563,0.1439016,0.0003105204,0.00005213581,0.0002970821,0.00002818492,0.0003865304],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7714131,"threshold_uncertainty_score":0.3857001,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2122272664","doi":"10.1016/s0167-7799(02)02035-8","title":"Functional CT: physiological models","year":2002,"lang":"en","type":"article","venue":"Trends in biotechnology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":167,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lawson Health Research Institute","funders":"","keywords":"Computed tomography; Functional imaging; Function (biology); Computer science; Medicine; Computational biology; Radiology; Biology; Evolutionary biology","retraction":null,"screen_n_in":null,"score":{"opus":0.1092986771249845,"gpt":0.325702173589172,"spread":0.2164034964641875,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005222104,0.00008988677,0.000184757,0.0002815612,0.00002899189,0.000002485941,0.00009988697,0.0001683118,0.002312827],"category_scores_gemma":[0.00002697708,0.0000704248,0.00005073687,0.0005023954,0.0002398548,0.00002338707,0.00005780536,0.0004145537,0.0001057446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003581562,"about_ca_system_score_gemma":0.000003660666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001069129,"about_ca_topic_score_gemma":0.000001505532,"domain_scores_codex":[0.9992687,0.000009146316,0.0001691159,0.0002525277,0.00009525428,0.0002052972],"domain_scores_gemma":[0.9995793,0.000014379,0.0000270819,0.0003140898,0.00001167704,0.00005346772],"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":[0.00002152808,0.001115542,0.0004887799,0.00001444223,0.00002457262,0.0001401583,0.00002589508,0.00002588934,0.05455047,0.2030753,0.3633921,0.3771253],"study_design_scores_gemma":[0.005474374,0.001226323,0.02607606,0.0001699616,0.0001121975,0.002036599,0.00008111475,0.3878133,0.05841975,0.1171644,0.4004636,0.0009623741],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5868304,0.0006801269,0.02014628,0.3387168,0.0001640501,0.0003539173,0.00001449507,0.002783374,0.05031047],"genre_scores_gemma":[0.9848403,0.0002000161,0.0115061,0.0009323106,0.00004702322,0.00006306667,0.00002361067,0.000008429796,0.002379101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3980099,"threshold_uncertainty_score":0.9985992,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2065218203","doi":"10.1007/s00330-012-2415-4","title":"Technical performance evaluation of a human brain PET/MRI system","year":2012,"lang":"en","type":"article","venue":"European Radiology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":165,"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":"Imaging phantom; Positron emission tomography; Image quality; Nuclear medicine; Medicine; Magnetic resonance imaging; Cardiac imaging; Biomedical engineering; Computer science; Radiology; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.05253407197625833,"gpt":0.3551741910847863,"spread":0.302640119108528,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002596391,0.0000766822,0.0001979163,0.00006391843,0.00004944236,0.00000176323,0.0001120417,0.00002702818,0.0001016102],"category_scores_gemma":[0.0001468989,0.0000616105,0.00004702822,0.00009811723,0.0001523388,0.00003081197,0.00004553587,0.0001660559,0.00008152713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006553319,"about_ca_system_score_gemma":0.000026554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002615188,"about_ca_topic_score_gemma":1.206518e-7,"domain_scores_codex":[0.9988831,0.0003177675,0.0002788168,0.0001347636,0.0001964135,0.0001891657],"domain_scores_gemma":[0.9992965,0.00004439832,0.00009691979,0.0003536695,0.00009183652,0.000116662],"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":[0.00006162571,0.0006334979,0.06794032,0.0004706811,0.00007278023,0.00003202719,0.0004329084,0.000007970511,0.6728685,0.04567574,0.1805217,0.03128221],"study_design_scores_gemma":[0.003774759,0.001397397,0.8602515,0.0007051412,0.0007004132,0.008372041,0.0002145081,0.01546557,0.009340309,0.00007084481,0.09922749,0.0004799911],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9277821,0.0001458269,0.002339649,0.001433101,0.00006531346,0.0004090307,0.000001875049,0.0002078188,0.06761525],"genre_scores_gemma":[0.9935616,0.000009120367,0.005631512,0.0002564142,0.0003071203,0.0000306933,0.00003101316,0.00001529556,0.000157232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7923113,"threshold_uncertainty_score":0.2512403,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1971331125","doi":"10.1007/s00270-012-0446-5","title":"Multimodality Image Fusion–Guided Procedures: Technique, Accuracy, and Applications","year":2012,"lang":"en","type":"review","venue":"CardioVascular and Interventional Radiology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":159,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"National Institutes of Health","keywords":"Image fusion; Medicine; Multimodality; Positron emission tomography; Magnetic resonance imaging; Radiology; Medical physics; Modality (human–computer interaction); PET-CT; Modalities; Computer vision; Artificial intelligence; Computer science; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.06225664626391785,"gpt":0.3902313567210308,"spread":0.3279747104571129,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009815377,0.0003720757,0.001773861,0.0001905949,0.0001417306,0.00002470839,0.0001791024,0.0004588613,0.0001519875],"category_scores_gemma":[0.0003255109,0.0002880934,0.001730053,0.0001793445,0.0005094563,0.00007359916,0.0002428169,0.0005705311,0.00002340238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008621644,"about_ca_system_score_gemma":0.0001657389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002506711,"about_ca_topic_score_gemma":5.40525e-7,"domain_scores_codex":[0.9978963,0.0002269229,0.0007154916,0.0006395044,0.0002216095,0.000300151],"domain_scores_gemma":[0.9984182,0.0001695445,0.0002008147,0.0007234075,0.0001757689,0.0003123075],"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":[0.000005561451,0.0002079775,0.00003992401,0.0318108,0.001709856,0.00001206023,0.000008173287,2.346256e-8,0.00001427771,0.003848575,0.003747404,0.9585954],"study_design_scores_gemma":[0.0002876038,0.00005200706,0.0001098651,0.003777402,0.003711402,0.00620583,0.000004225087,0.000008160418,0.000004701257,0.0005796853,0.9850082,0.0002508586],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000006481223,0.9201676,0.07595638,0.0001205699,0.00004294375,0.002962366,0.00008144305,0.0001322026,0.0005299831],"genre_scores_gemma":[0.0002703112,0.980446,0.01218622,0.00007980302,0.0005389996,0.005502548,0.000771735,0.00004088831,0.0001634978],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9812608,"threshold_uncertainty_score":0.9999571,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}