{"id":"W2916399584","doi":"10.1287/inte.2018.0969","title":"Automated Pathologist Scheduling at The Ottawa Hospital","year":2019,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Electricity Association; Ottawa Hospital; University of Ottawa","funders":"Department of Pathology and Laboratory Medicine, University of North Carolina School of Medicine","keywords":"Scheduling (production processes); Medical laboratory; Medicine; Computer science; Medical physics; Medical emergency; Pathology; Operations management; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002169803,0.0007063611,0.0003897216,0.00176849,0.002314232,0.001956262,0.001156633,0.0006353492,0.007463896],"category_scores_gemma":[0.00636617,0.0007215363,0.0004773285,0.001776545,0.0005792312,0.0006921488,0.0009801507,0.0007604804,0.001688208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01308478,"about_ca_system_score_gemma":0.02630892,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5083251,"about_ca_topic_score_gemma":0.575642,"domain_scores_codex":[0.9975593,0.0005369931,0.0001912465,0.0006042818,0.0006737915,0.0004344011],"domain_scores_gemma":[0.992718,0.001959604,0.0005955801,0.0006218366,0.002319125,0.001785786],"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":[0.004672007,0.0007841157,0.09153824,0.0004766971,0.0002747818,0.00174051,0.002532708,0.1927607,0.02956425,0.005038626,0.1764812,0.4941362],"study_design_scores_gemma":[0.0007804291,0.0006833726,0.1034703,0.0001382648,0.0001795516,0.0005158837,0.003715643,0.7004023,0.03077357,0.004899813,0.1540547,0.0003861474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7974834,0.002601519,0.1039935,0.01255641,0.001313757,0.001860931,0.01489143,0.02381028,0.04148884],"genre_scores_gemma":[0.8661885,0.0006768446,0.1086232,0.0006696912,0.0001709889,0.0001684806,0.005588057,0.0005495046,0.0173648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5083251,"threshold_uncertainty_score":0.9891409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03070036650754712,"score_gpt":0.3653847770548557,"score_spread":0.3346844105473086,"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."}}