{"id":"W6902258680","doi":"10.6084/m9.figshare.c.5531059.v1","title":"Assessing record linkage between health care and Vital Statistics databases using deterministic methods","year":2021,"lang":"en","type":"other","venue":"Figshare","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Health Economics; University of Calgary","funders":"","keywords":"Linkage (software); Record linkage; Identifier; Population; Health care; Vital rates","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":[0.1599224,0.0006926687,0.001219015,0.01063679,0.002013444,0.004440757,0.002371912,0.001101871,0.00289066],"category_scores_gemma":[0.4197625,0.0007195035,0.001910064,0.0167199,0.001235644,0.002234938,0.004113694,0.0008175552,0.0005452234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005748861,"about_ca_system_score_gemma":0.01295237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1080499,"about_ca_topic_score_gemma":0.07917034,"domain_scores_codex":[0.8022865,0.1367139,0.015244,0.01157399,0.03179619,0.002385481],"domain_scores_gemma":[0.5787371,0.3156798,0.04236277,0.02984664,0.03237719,0.0009965202],"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":[0.0007360814,0.000121576,0.7924921,0.001521157,0.004133029,0.0001553538,0.001796951,0.02514154,0.0004733872,0.009964716,0.007165473,0.1562987],"study_design_scores_gemma":[0.0004895441,0.0007279674,0.6746203,0.001595931,0.003810406,0.001286513,0.003218689,0.2357903,0.008505605,0.02973098,0.03963355,0.0005903131],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5020806,0.005507331,0.4424697,0.002627561,0.0001963064,0.003251784,0.02755501,0.001604273,0.01470737],"genre_scores_gemma":[0.8066794,0.0009611449,0.1809165,0.0002845312,0.00005371331,0.001383636,0.008716225,0.0001455403,0.0008592163],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1599224,"threshold_uncertainty_score":0.8457604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3463707465961666,"score_gpt":0.5229437853347912,"score_spread":0.1765730387386247,"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."}}