{"id":"W27510588","doi":"","title":"Computerized O.R. scheduling: is it an accurate predictor of surgical time?","year":2001,"lang":"en","type":"article","venue":"PubMed","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary General Hospital","funders":"","keywords":"Scheduling (production processes); Computer science; Surgical procedures; Medicine; Statistics; Surgery; Operations management; Mathematics; Engineering","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":[0.003333564,0.0002705202,0.0003316126,0.0009779059,0.0001814188,0.0006140964,0.0003419692,0.0003055711,0.001936383],"category_scores_gemma":[0.04326747,0.0001222431,0.0002542982,0.001966138,0.0002419815,0.0006240901,0.000206968,0.0003522701,0.0004956273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005594533,"about_ca_system_score_gemma":0.00177518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008558869,"about_ca_topic_score_gemma":0.007219683,"domain_scores_codex":[0.9980167,0.0009722324,0.0001890705,0.0002244163,0.0004746518,0.0001228958],"domain_scores_gemma":[0.9586987,0.02319141,0.01202956,0.001977892,0.003232565,0.0008698368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003621377,0.000131359,0.9114042,0.00006083164,0.00004204989,0.00002508102,0.00005001021,0.004785952,0.0003567131,0.0003372214,0.001036554,0.08140797],"study_design_scores_gemma":[0.00008887496,0.001539529,0.9367511,0.00008358376,0.0001017798,0.0002768176,0.000323346,0.05454543,0.00205537,0.001513758,0.002695293,0.00002516643],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771405,0.001470412,0.01546232,0.00147267,0.0001370955,0.00008270878,0.0009554626,0.0001511436,0.003127566],"genre_scores_gemma":[0.9918144,0.0004455235,0.006823746,0.0000898613,0.00007570942,0.00002288706,0.0003640504,0.00001817851,0.0003456362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008558869,"threshold_uncertainty_score":0.0176298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.125800783036833,"score_gpt":0.4008847574502982,"score_spread":0.2750839744134652,"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."}}