{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02228882,0.0001900214,0.0005565425,0.0002045122,0.0008309854,0.00004712027,0.001056859,0.000200662,0.00009485022],"category_scores_gemma":[0.03108886,0.0001822885,0.00009267728,0.0002043578,0.00002776443,0.0007359839,0.0004367288,0.001303263,0.000006366428],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001540396,"about_ca_system_score_gemma":0.007000501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001435822,"about_ca_topic_score_gemma":0.006263963,"domain_scores_codex":[0.9920308,0.002753574,0.003311682,0.0004999619,0.000721568,0.000682417],"domain_scores_gemma":[0.9870242,0.008835243,0.001717166,0.0006764931,0.001370089,0.0003768341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004553468,0.001038235,0.1501262,0.04529016,0.0005200521,0.0001565824,0.2410439,0.004255974,0.000095166,0.01939504,0.08525271,0.4482725],"study_design_scores_gemma":[0.01206058,0.0005910689,0.01934957,0.01258814,0.00004401249,0.0001011508,0.1803077,0.6845834,0.00007359205,0.007562734,0.08208001,0.000658064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1039472,0.0013395,0.7606819,0.09621649,0.01675891,0.004879125,0.01573804,0.00009664489,0.0003421381],"genre_scores_gemma":[0.3039447,0.0002768288,0.6645074,0.01386946,0.003103259,0.0004906833,0.01365709,0.00004396345,0.0001065899],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6803274,"threshold_uncertainty_score":0.9986289,"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."}}