{"id":"W4281560254","doi":"10.1016/j.jmir.2022.04.026","title":"Capturing Patients’ Radiation Therapy Appointment Time Preferences for Incorporation into an AI Auto-Scheduling Program","year":2022,"lang":"en","type":"article","venue":"Journal of medical imaging and radiation sciences","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre","funders":"","keywords":"Factoring; Schedule; Computer science; Scheduling (production processes); CLIPS; Time constraint; Operations research; Operations management; Artificial intelligence; Engineering; Operating system; Business","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":[0.0006349488,0.0002837145,0.0002572577,0.0006845196,0.0002059236,0.0005361059,0.0003546875,0.0002259959,0.002388086],"category_scores_gemma":[0.004406963,0.0001396829,0.0002257536,0.0008508798,0.00005557271,0.0003603856,0.0001829553,0.000436434,0.0003500671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003966355,"about_ca_system_score_gemma":0.0009549582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005840467,"about_ca_topic_score_gemma":0.0133126,"domain_scores_codex":[0.9997229,0.00008711648,0.0000262962,0.0000669267,0.0000630635,0.00003361091],"domain_scores_gemma":[0.9978086,0.00121017,0.0003045398,0.0001142371,0.0003233053,0.0002391345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003199873,0.002531154,0.6253361,0.0001618261,0.0002372131,0.0001890429,0.0004776414,0.07127923,0.01186908,0.0007749989,0.007525726,0.2764182],"study_design_scores_gemma":[0.00006864161,0.0009303667,0.1752183,0.00001852432,0.00009801598,0.0001876823,0.0008499642,0.8136917,0.005461093,0.0009172224,0.002516594,0.00004195177],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745905,0.00009540607,0.02025728,0.0003175027,0.00003306324,0.0001057392,0.001397448,0.000307907,0.002895231],"genre_scores_gemma":[0.9813682,0.00003672886,0.01726938,0.00004920676,0.00001349073,0.00003940815,0.0007521641,0.00001690376,0.0004545529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005840467,"threshold_uncertainty_score":0.01161295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05281385635321166,"score_gpt":0.4314587602211905,"score_spread":0.3786449038679788,"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."}}