{"id":"W2533963320","doi":"10.1186/s12911-016-0375-3","title":"Describing the linkages of the immigration, refugees and citizenship Canada permanent resident data and vital statistics death registry to Ontario’s administrative health database","year":2016,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":204,"is_retracted":false,"has_abstract":true,"ca_institutions":"College of Physicians and Surgeons of Ontario; Ottawa Hospital; Canadian Institute for Health Information; Institute for Clinical Evaluative Sciences","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences","keywords":"Health informatics; Refugee; Citizenship; Immigration; Health statistics; Database; Health services research; Official statistics; Nationality; Data science; Medicine; Public health; Political science; Computer science; Nursing; Environmental health; Law; Population","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0117056,0.0002834639,0.0003040043,0.005423367,0.001868864,0.002408399,0.001376287,0.0005589139,0.003959452],"category_scores_gemma":[0.05046941,0.0004285527,0.0006970373,0.01606398,0.0004507343,0.001178757,0.001588723,0.0005025273,0.0008547079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02651789,"about_ca_system_score_gemma":0.06810357,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9178211,"about_ca_topic_score_gemma":0.9421678,"domain_scores_codex":[0.9905818,0.001906809,0.001711562,0.001123576,0.003783436,0.0008927665],"domain_scores_gemma":[0.9674709,0.007707139,0.006563998,0.002787158,0.0148011,0.000669758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002169066,0.00006961466,0.7081906,0.00164534,0.000345111,0.0005803438,0.004753777,0.01417238,0.0007568882,0.01624645,0.1186037,0.1344189],"study_design_scores_gemma":[0.00008143588,0.0000712072,0.6540838,0.001222448,0.0002401536,0.0003771663,0.003439537,0.01745227,0.001605644,0.002238808,0.3190435,0.000144038],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2504072,0.002675253,0.05435567,0.00796928,0.0002682955,0.004228336,0.6370353,0.001186228,0.04187454],"genre_scores_gemma":[0.5882051,0.00399848,0.07202266,0.0009289588,0.0000891583,0.004545977,0.3158886,0.0003018302,0.0140192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08217889,"threshold_uncertainty_score":0.1924016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2748628273168988,"score_gpt":0.4274036063539242,"score_spread":0.1525407790370253,"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."}}