{"id":"W2125389120","doi":"10.1177/0272989x04265483","title":"From Diagnostic Accuracy to Accurate Diagnosis: Interpreting a Test Result with Confidence","year":2004,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Statistics; Confidence interval; Logit; Logistic regression; Statement (logic); Transformation (genetics); Computer science; Sample (material); Positive predicative value; Test (biology); Econometrics; Diagnostic accuracy; Pre- and post-test probability; Mathematics; Data mining; Predictive value; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.1044637,0.001124315,0.00146273,0.008369097,0.0008682192,0.006436898,0.003062572,0.003146725,0.003957625],"category_scores_gemma":[0.5782363,0.0005596256,0.001070835,0.005275483,0.01041497,0.005828041,0.006142,0.004387595,0.0008862634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003129501,"about_ca_system_score_gemma":0.00361112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001550705,"about_ca_topic_score_gemma":0.0006931694,"domain_scores_codex":[0.808347,0.1397328,0.01528026,0.005007975,0.03032465,0.001307203],"domain_scores_gemma":[0.4136822,0.5173208,0.02694838,0.01930727,0.02083591,0.00190548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001323925,0.0002172387,0.05596642,0.005241067,0.0007380561,0.0008964147,0.005641837,0.01783844,0.00223583,0.2487357,0.01703972,0.6441252],"study_design_scores_gemma":[0.0002289209,0.001068839,0.02590198,0.005670097,0.0007554104,0.004295266,0.002881223,0.08248228,0.0110604,0.8356869,0.02956045,0.0004081337],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03581131,0.01007273,0.9147328,0.0187,0.001040561,0.0007251567,0.0007024208,0.0007040139,0.01751107],"genre_scores_gemma":[0.6465434,0.002788311,0.3461014,0.002312664,0.0006223053,0.0009478024,0.0002588347,0.0001000341,0.0003251825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1044637,"threshold_uncertainty_score":0.5524635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.202435274144168,"score_gpt":0.48219096307565,"score_spread":0.279755688931482,"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."}}