{"id":"W2735217689","doi":"10.1287/ijoc.2017.0745","title":"Collaborative Operating Room Planning and Scheduling","year":2017,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"","keywords":"Schedule; Operations research; Scheduling (production processes); Operations management; Computer science; Profitability index; Health care; Flexibility (engineering); Business; Economics; 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":[0.001819567,0.0009108693,0.0008886966,0.0006669928,0.0007078953,0.001813353,0.002103459,0.001027024,0.008355415],"category_scores_gemma":[0.003516981,0.0008004573,0.001196198,0.001023503,0.0009428944,0.001475127,0.001547014,0.00125514,0.0006265034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002149459,"about_ca_system_score_gemma":0.003670149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01045855,"about_ca_topic_score_gemma":0.01057122,"domain_scores_codex":[0.9976944,0.0007362162,0.0001016677,0.0005017688,0.0004766838,0.0004892359],"domain_scores_gemma":[0.9982337,0.0008891178,0.0002837872,0.000184913,0.0002195985,0.0001888773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008269375,0.00005652911,0.0006268948,0.00005426271,0.00003216474,0.00007879886,0.00007334382,0.9501904,0.001015133,0.02773672,0.001338911,0.0187141],"study_design_scores_gemma":[0.00002752163,0.0000500869,0.0003354542,0.000007494275,0.000014783,0.00003310785,0.00006237269,0.9778208,0.0007415203,0.01870703,0.002185523,0.00001435609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03081637,0.0001606198,0.953745,0.0003590427,0.00004730375,0.0001897014,0.0002701464,0.0002269684,0.01418488],"genre_scores_gemma":[0.739799,0.0003505246,0.2491458,0.0001393449,0.00005252707,0.0003066431,0.0005451972,0.00009905041,0.009561906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01045855,"threshold_uncertainty_score":0.0279516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07147119202570347,"score_gpt":0.4536454377199059,"score_spread":0.3821742456942024,"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."}}