{"id":"W4200405701","doi":"10.1016/j.injury.2021.12.016","title":"Registries: Big data, bigger problems?","year":2021,"lang":"en","type":"article","venue":"Injury","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Generalizability theory; Data quality; Big data; Population; Quality (philosophy); Computer science; Health care; Medicine; Data science; Patient registry; Data mining; Psychology; Pediatrics; Metric (unit); Operations management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002012482,0.00006926261,0.0001290179,0.00004509055,0.0000717665,0.00002382633,0.00009988966,0.00007995217,0.0003358245],"category_scores_gemma":[0.0005471705,0.00006305762,0.00002686688,0.0002728881,0.00005230892,0.00008345336,0.00006475584,0.0001580046,0.0004466888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004586634,"about_ca_system_score_gemma":0.0006122396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003931628,"about_ca_topic_score_gemma":0.0001094191,"domain_scores_codex":[0.999099,0.00002756899,0.0002629904,0.0002721923,0.0001582374,0.0001799972],"domain_scores_gemma":[0.9987077,0.00006183295,0.000048365,0.0008447724,0.0002258364,0.0001114519],"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":[0.00005158218,0.0002004031,0.0196754,0.0001821292,0.00002853533,0.00003645722,0.000875003,7.75734e-7,0.00455386,0.0003313379,0.1528797,0.8211849],"study_design_scores_gemma":[0.00003096608,0.0001264025,0.001861631,0.0001657625,0.00004092777,0.00007165538,0.001310079,0.00008533337,0.06883667,0.00176918,0.9255797,0.0001217354],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9106899,0.00279635,0.00183695,0.05484512,0.005869113,0.0005563585,0.000106877,0.0002287073,0.02307061],"genre_scores_gemma":[0.9864815,0.0003743124,0.0006148309,0.002696935,0.001871989,0.00001621673,0.000382395,0.00001677362,0.007544972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8210631,"threshold_uncertainty_score":0.5741429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3643780621539353,"score_gpt":0.4511892384814056,"score_spread":0.08681117632747026,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}