{"meta":{"query_hash":"f826e428d751","filters":{"venue":"Digital Medicine"},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/f826e428d751","api":"https://metacan.xera.ac/api/v1/cohort?venue=Digital+Medicine"},"results":[{"id":"W2636410642","doi":"10.4103/digm.digm_8_17","title":"Mapping three-dimensional digital model to surgical site in facial surgery","year":2017,"lang":"en","type":"article","venue":"Digital Medicine","topic":"Reconstructive Surgery and Microvascular Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cheek; Merge (version control); Computer vision; Computer science; Medicine; Virtual reality; 3d model; Surgery; Artificial intelligence","score_opus":0.04221531745778488,"score_gpt":0.29031508291640845,"score_spread":0.24809976545862356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2636410642","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46547753,0.0012898386,0.5156876,0.00072217063,0.00028719677,0.00026689513,0.0008202913,0.0021194462,0.013329014],"genre_scores_gemma":[0.88893956,0.0007795629,0.10654651,0.00008498286,0.000024538047,0.000077021214,0.00026824427,0.00009424866,0.0031852138],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997017,0.000061397935,0.000023671872,0.000034755434,0.00015346784,0.000024942034],"domain_scores_gemma":[0.9996519,0.00010061862,0.000040688854,0.000114000395,0.00006377887,0.000029028335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028358743,0.00031787044,0.0001396398,0.0009844205,0.00016516069,0.00084994064,0.0003471354,0.00054281217,0.0031862357],"category_scores_gemma":[0.0012634636,0.00032976925,0.00040986936,0.0004033389,0.00037314196,0.00066032377,0.00077860855,0.00040648066,0.0008189813],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007255392,0.0002918417,0.058198772,0.00075721036,0.00011016511,0.010113485,0.0019436195,0.04962504,0.31239426,0.007983231,0.009204548,0.5486523],"study_design_scores_gemma":[0.00012669474,0.0013722158,0.092093445,0.00044270302,0.0003076066,0.07974016,0.00209781,0.40466863,0.33126152,0.0104751345,0.0770147,0.00039943238],"about_ca_topic_score_codex":0.0014337534,"about_ca_topic_score_gemma":0.0013796608,"teacher_disagreement_score":0.0031862357,"about_ca_system_score_codex":0.00025185393,"about_ca_system_score_gemma":0.00047735035,"threshold_uncertainty_score":0.010658979},"labels":[],"label_agreement":null},{"id":"W2803414055","doi":"10.4103/digm.digm_9_18","title":"Digital medicine: Emergence, definition, scope, and future","year":2018,"lang":"en","type":"article","venue":"Digital Medicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Zhàng; Scope (computer science); China; Editorial board; Library science; Editor in chief; Medicine; Political science; Management; Computer science; Law","score_opus":0.1177993153318797,"score_gpt":0.39441022563780365,"score_spread":0.27661091030592394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803414055","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012999341,0.5551178,0.01476893,0.331553,0.005704686,0.000094500945,0.00026869183,0.00021731331,0.07927574],"genre_scores_gemma":[0.37590367,0.53363514,0.020172397,0.044214085,0.014871687,0.00037092093,0.00038488358,0.00020117944,0.010246018],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9942726,0.0021308511,0.0006167843,0.00089709833,0.001495606,0.0005870883],"domain_scores_gemma":[0.9821476,0.008186568,0.0013532768,0.0012501437,0.0036931052,0.0033693996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01030105,0.0006719614,0.0010816184,0.0066437465,0.0028611177,0.01677101,0.001623637,0.004793629,0.003419435],"category_scores_gemma":[0.010897572,0.0005847408,0.000554301,0.0064202715,0.021483865,0.02511381,0.0073348857,0.00811606,0.00087753386],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007634521,0.00007597342,0.006262923,0.0015534676,0.000032389027,0.00029877076,0.00744207,0.00026472122,0.00062663545,0.60885084,0.040661883,0.33385402],"study_design_scores_gemma":[0.00002684307,0.00015305643,0.009005588,0.0064685764,0.000032022763,0.0034403051,0.019706147,0.0013912185,0.00041115787,0.28195095,0.6772733,0.00014084589],"about_ca_topic_score_codex":0.0024646183,"about_ca_topic_score_gemma":0.0025881135,"teacher_disagreement_score":0.01677101,"about_ca_system_score_codex":0.005419022,"about_ca_system_score_gemma":0.00940814,"threshold_uncertainty_score":0.05447781},"labels":[],"label_agreement":null},{"id":"W2803431205","doi":"10.4103/digm.digm_44_17","title":"A variational level set method image segmentation model with application to intensity inhomogene magnetic resonance imaging","year":2018,"lang":"en","type":"article","venue":"Digital Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Image segmentation; Artificial intelligence; Regularization (linguistics); Computer science; Level set (data structures); Level set method; Scale-space segmentation; Smoothing; Computer vision; Mathematics; Pattern recognition (psychology); Algorithm","score_opus":0.025646053573819362,"score_gpt":0.31998410171699276,"score_spread":0.2943380481431734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803431205","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006585333,0.00019753011,0.991968,0.00023693126,0.000022573959,0.000033645396,0.000028171175,0.00014588256,0.0007820232],"genre_scores_gemma":[0.48445094,0.00069588923,0.5056659,0.00030151996,0.00011212654,0.0003966956,0.00033621397,0.00035170597,0.0076888944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995172,0.0001333196,0.000026491889,0.00013706984,0.00013771672,0.00004819586],"domain_scores_gemma":[0.9994549,0.00024830695,0.000053761065,0.000035512414,0.00016444846,0.00004312603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013411894,0.000744496,0.0009892606,0.0012048131,0.00056570204,0.0011976326,0.0021872104,0.0020080542,0.0015657196],"category_scores_gemma":[0.002098812,0.00063679117,0.001458136,0.00076941797,0.0010387829,0.0011452017,0.001123412,0.0014249635,0.0003579072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004291692,0.000027299926,0.00059284543,0.000090534835,0.000055091125,0.00008143275,0.000107376596,0.936476,0.0058333226,0.025674185,0.0011080988,0.029910916],"study_design_scores_gemma":[0.0000015598131,0.0000060954817,0.000028073036,0.0000022556962,0.00000343364,0.000009557547,0.0000019119032,0.99804926,0.00030480357,0.001356981,0.00023286429,0.0000032025102],"about_ca_topic_score_codex":0.009246758,"about_ca_topic_score_gemma":0.004826409,"teacher_disagreement_score":0.009246758,"about_ca_system_score_codex":0.0017514486,"about_ca_system_score_gemma":0.0014740181,"threshold_uncertainty_score":0.018385887},"labels":[],"label_agreement":null},{"id":"W2803973046","doi":"10.4103/digm.digm_2_18","title":"Pulse wave analysis for cardiovascular disease diagnosis","year":2018,"lang":"en","type":"article","venue":"Digital Medicine","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Medicine; Pulse Wave Analysis; Pulse (music); Disease; Pulse wave; Cardiology; Internal medicine; Pulse wave velocity; Physical therapy; Blood pressure; Computer science; Telecommunications","score_opus":0.025623534269115983,"score_gpt":0.2327317276420122,"score_spread":0.2071081933728962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803973046","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74102426,0.03884915,0.19121772,0.0024810173,0.0010148357,0.0005062049,0.004086985,0.0010892354,0.019730626],"genre_scores_gemma":[0.93453526,0.0061555007,0.05522217,0.00030894572,0.00049025944,0.00015021773,0.0010829683,0.00003475149,0.002019912],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999212,0.00037466604,0.00008409699,0.00009534728,0.00020806622,0.000025770332],"domain_scores_gemma":[0.9980414,0.0010745345,0.00029878123,0.00015116422,0.00033666,0.00009747133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014215512,0.00048024365,0.00035189607,0.0022480856,0.00018859307,0.00063684187,0.00034786045,0.00034246987,0.0026557357],"category_scores_gemma":[0.0029244495,0.000110860485,0.00024031728,0.0010727273,0.0003236629,0.00037359202,0.00029733247,0.00064868724,0.0008521984],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001013643,0.0004951301,0.40818894,0.00085261377,0.00024603773,0.0008307338,0.0002521807,0.0008826938,0.041946642,0.0015889156,0.0042904937,0.539412],"study_design_scores_gemma":[0.00014551292,0.002238652,0.89898616,0.00039095333,0.0006301527,0.008264975,0.0006250897,0.02312234,0.037364606,0.0062466357,0.021905622,0.00007930513],"about_ca_topic_score_codex":0.0003999479,"about_ca_topic_score_gemma":0.00043747886,"teacher_disagreement_score":0.0026557357,"about_ca_system_score_codex":0.00017437832,"about_ca_system_score_gemma":0.00043785415,"threshold_uncertainty_score":0.00888437},"labels":[],"label_agreement":null},{"id":"W2975965984","doi":"10.4103/digm.digm_7_19","title":"Local Gauss multiplicative components method for brain magnetic resonance image segmentation","year":2019,"lang":"en","type":"article","venue":"Digital Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Artificial intelligence; Scale-space segmentation; Computer science; Image segmentation; Pattern recognition (psychology); Contrast (vision); Multiplicative function; Image (mathematics); Segmentation-based object categorization; Gaussian; Computer vision; Mathematics; Physics","score_opus":0.017570214429172188,"score_gpt":0.32719827294939785,"score_spread":0.3096280585202257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975965984","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002636267,0.00020655594,0.99628794,0.00006149757,0.000020776652,0.00003192756,0.000020877827,0.00033067976,0.00040349018],"genre_scores_gemma":[0.15494318,0.0009616339,0.8382703,0.00019934612,0.00014616888,0.00025112528,0.00032455346,0.00049661624,0.0044069677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939847,0.00014331331,0.000031707135,0.00013821559,0.00023892857,0.00004947682],"domain_scores_gemma":[0.9994923,0.0001709811,0.000056825425,0.00005391218,0.00019255684,0.00003336392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008909468,0.00103123,0.0009011552,0.0019602804,0.00042369025,0.000998822,0.0013694832,0.0011827461,0.0019834351],"category_scores_gemma":[0.0017848383,0.00053642667,0.001346767,0.0012160735,0.00078336895,0.00094490487,0.00082848134,0.0010912664,0.00094763614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022995938,0.000101000354,0.0015967004,0.0003618678,0.00021982695,0.00016249357,0.00025255373,0.48419395,0.027802177,0.032531694,0.005228139,0.4473197],"study_design_scores_gemma":[0.0000046457176,0.000016866199,0.00017123825,0.000007672477,0.00002140865,0.000034206132,0.000009464564,0.99217844,0.0030283255,0.0030183208,0.0014984823,0.00001085009],"about_ca_topic_score_codex":0.006967341,"about_ca_topic_score_gemma":0.0058349213,"teacher_disagreement_score":0.006967341,"about_ca_system_score_codex":0.001047395,"about_ca_system_score_gemma":0.0015377465,"threshold_uncertainty_score":0.01385355},"labels":[],"label_agreement":null}]}