{"id":"W7009068239","doi":"","title":"Do administrative databases accurately measure waiting times for medical care? Evidence from general surgery","year":2007,"lang":"en","type":"article","venue":"PubMed Central","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medical record; Measure (data warehouse); Waiting list; Hospital records; Patient data; Gold standard (test)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004163329,0.0002053098,0.0003481771,0.00008705944,0.001000995,0.00004697601,0.0001899663,0.0002719295,0.0006189448],"category_scores_gemma":[0.01134736,0.0001793932,0.0001049125,0.000223571,0.00006052037,0.0004300989,0.00005541253,0.0005369501,0.0000206831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004406175,"about_ca_system_score_gemma":0.003428261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001073695,"about_ca_topic_score_gemma":0.003140818,"domain_scores_codex":[0.9956068,0.0005208069,0.0009382971,0.0004691331,0.0005943459,0.001870636],"domain_scores_gemma":[0.9931446,0.004829661,0.0002479329,0.0002961226,0.0004810892,0.001000623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007897686,0.0001420006,0.8284779,0.0004878188,0.0001254454,0.0000720021,0.01591135,0.0003271746,0.0000878271,0.005118067,0.01951675,0.1289439],"study_design_scores_gemma":[0.002496437,0.0001096703,0.9103025,0.00318655,0.000184337,0.00001209247,0.02687162,0.01820525,0.002103724,0.0001469709,0.03502238,0.001358531],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9169104,0.002006795,0.06912958,0.005111291,0.00327934,0.002172811,0.0005351106,0.0001670916,0.000687654],"genre_scores_gemma":[0.9796187,0.0002475032,0.01394632,0.001102761,0.003322539,0.000506854,0.001029547,0.00003539232,0.0001903945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1275854,"threshold_uncertainty_score":0.9969805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3232133063294714,"score_gpt":0.4728273880074921,"score_spread":0.1496140816780207,"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."}}