{"id":"W4412009847","doi":"10.2196/73916","title":"Comparing Multiple Imputation Methods to Address Missing Patient Demographics in Immunization Information Systems: Retrospective Cohort Study","year":2025,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Demographics; Imputation (statistics); Missing data; Medicine; Cohort; Statistics; Computer science; Data science; Demography; Mathematics; World Wide Web; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06454482,0.00080248,0.0009929262,0.001757791,0.0009336428,0.001455342,0.002557719,0.001363106,0.002137871],"category_scores_gemma":[0.1283197,0.0007959654,0.004694841,0.002714348,0.0005019394,0.001434008,0.001676353,0.002317559,0.0005182039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001198423,"about_ca_system_score_gemma":0.002769561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01121886,"about_ca_topic_score_gemma":0.01074968,"domain_scores_codex":[0.9592542,0.03022135,0.002645076,0.003898044,0.002756267,0.001225052],"domain_scores_gemma":[0.9132964,0.05063037,0.01227301,0.01584613,0.006642243,0.001311927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00222668,0.0004195065,0.9596037,0.0003014502,0.005887962,0.00008982665,0.0006711428,0.003401683,0.000123516,0.001285835,0.003552376,0.02243629],"study_design_scores_gemma":[0.001714883,0.005581966,0.8603584,0.0009769989,0.008747146,0.001110756,0.002495382,0.1019683,0.001285132,0.004034463,0.0114931,0.0002335475],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9170299,0.002937837,0.06889366,0.001092486,0.0002836252,0.0009432856,0.007548006,0.000178131,0.001093079],"genre_scores_gemma":[0.960171,0.000782807,0.03139632,0.0005566094,0.0001392081,0.001445115,0.004961928,0.0001010451,0.0004459926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06454482,"threshold_uncertainty_score":0.3413497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02686080579150256,"score_gpt":0.360973797100699,"score_spread":0.3341129913091964,"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."}}