{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07948654,0.001772863,0.003637891,0.009062241,0.002789321,0.01982175,0.005996827,0.01177649,0.02180842],"category_scores_gemma":[0.2243584,0.001541121,0.002252375,0.01753188,0.0122557,0.05509803,0.007889577,0.01696092,0.004444434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005851436,"about_ca_system_score_gemma":0.0117309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0154319,"about_ca_topic_score_gemma":0.01946049,"domain_scores_codex":[0.9498723,0.03290387,0.004463937,0.004455369,0.006759179,0.001545464],"domain_scores_gemma":[0.5711141,0.3040742,0.02604421,0.0403762,0.03831967,0.02007166],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004453428,0.0001561678,0.0367499,0.002607126,0.0007000075,0.0003361528,0.001292805,0.001481599,0.0001348961,0.1745754,0.611801,0.1697196],"study_design_scores_gemma":[0.000143824,0.00006742735,0.008901447,0.006860348,0.0004287707,0.001041977,0.008348147,0.004985971,0.0002242547,0.6420369,0.3267623,0.0001985817],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.003616068,0.03929038,0.0124456,0.9265811,0.006177112,0.00006418428,0.004946931,0.0004251226,0.006453454],"genre_scores_gemma":[0.2537858,0.1364537,0.09542719,0.4256296,0.06307233,0.0006065473,0.01529646,0.001584272,0.008144075],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9205135,"threshold_uncertainty_score":0.42037,"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."}}