{"id":"W2891964161","doi":"10.23889/ijpds.v3i4.710","title":"Internal and External Data Linkage of Complex Relational Database: Results from CorHealth Ontario","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences","funders":"","keywords":"Relational database; Database; Computer science; Record linkage; Linkage (software); Referral; Data mining; Cohort; Medicine; Population; Family medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0268597,0.000844078,0.0007444918,0.002690094,0.001901291,0.003657425,0.002059939,0.000620508,0.006447886],"category_scores_gemma":[0.08579388,0.0003988337,0.001792564,0.008169985,0.001057804,0.002483675,0.003650218,0.0006698907,0.00121891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01106791,"about_ca_system_score_gemma":0.01669546,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5793413,"about_ca_topic_score_gemma":0.4416638,"domain_scores_codex":[0.9649353,0.0107716,0.003435825,0.004585322,0.01449725,0.001774751],"domain_scores_gemma":[0.9117778,0.03092517,0.004453556,0.01653048,0.03497057,0.001342474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003895716,0.001253387,0.5080642,0.004185272,0.002435646,0.001001903,0.008017083,0.01816383,0.006662756,0.01081637,0.2145185,0.2209854],"study_design_scores_gemma":[0.001497369,0.0009485395,0.6377344,0.000936286,0.001974419,0.001009519,0.01084783,0.07491356,0.01672382,0.003703323,0.2493719,0.0003390854],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6462432,0.002835726,0.03202965,0.005072108,0.0002507808,0.002961609,0.2699291,0.008535787,0.03214207],"genre_scores_gemma":[0.6011286,0.001049742,0.06934024,0.000580326,0.00007204061,0.001298946,0.3201375,0.0009477398,0.005444929],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4206587,"threshold_uncertainty_score":0.8462721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2834305550450701,"score_gpt":0.46123493804698,"score_spread":0.1778043830019099,"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."}}