{"id":"W3128045780","doi":"10.1093/intqhc/mzab025","title":"Mitigating imperfect data validity in administrative data PSIs: a method for estimating true adverse event rates","year":2021,"lang":"en","type":"article","venue":"International Journal for Quality in Health Care","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Health Information; University of Calgary","funders":"Agence Nationale de la Recherche","keywords":"Statistics; Computer science; Coding (social sciences); Data quality; Measure (data warehouse); Bayesian probability; Data mining; Mathematics; 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":["metaresearch"],"category_scores_codex":[0.2021064,0.001083408,0.001556909,0.003406047,0.001379202,0.004665149,0.004017188,0.002809983,0.002037199],"category_scores_gemma":[0.549735,0.001243703,0.002311905,0.004212257,0.004419028,0.005137178,0.005092507,0.003531103,0.0005144218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002823574,"about_ca_system_score_gemma":0.005084056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005549843,"about_ca_topic_score_gemma":0.003564953,"domain_scores_codex":[0.7595541,0.2060357,0.00897651,0.009865205,0.01464931,0.0009190416],"domain_scores_gemma":[0.4364362,0.4187236,0.05347208,0.06834231,0.02190066,0.001125108],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001123406,0.0004267834,0.249381,0.001323926,0.003530637,0.0003358466,0.006103315,0.12897,0.002254597,0.1824948,0.008140029,0.4159156],"study_design_scores_gemma":[0.0008190734,0.001130159,0.06227426,0.001425119,0.001010249,0.001135421,0.001311222,0.6752158,0.007601666,0.2329124,0.01471673,0.0004478766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01696637,0.0001659167,0.9794057,0.001581369,0.00007861957,0.0004318866,0.0002417173,0.00021358,0.0009148294],"genre_scores_gemma":[0.3327495,0.0001508318,0.6641598,0.0006985819,0.0001479151,0.001189381,0.0003603254,0.00007489364,0.0004688097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7978936,"threshold_uncertainty_score":0.9839448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7011892441030876,"score_gpt":0.686240428518897,"score_spread":0.01494881558419059,"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."}}