{"id":"W7132987834","doi":"","title":"Magnetic Resonance Imaging Schedule Optimization at JDMI","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Magnetic resonance imaging; Medical imaging; Scheduling (production processes); Schedule; Mri scan; Flexibility (engineering)","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":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002024095,0.0009915112,0.00104551,0.0006947442,0.009477176,0.0001750626,0.0006753608,0.0009651987,0.1020676],"category_scores_gemma":[0.001140706,0.001189945,0.0002785084,0.001765116,0.0001168299,0.0004443908,0.0004333342,0.003082037,0.00103529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002708596,"about_ca_system_score_gemma":0.003483072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001919798,"about_ca_topic_score_gemma":0.001147653,"domain_scores_codex":[0.9904159,0.002689015,0.002148195,0.001773599,0.001307762,0.001665498],"domain_scores_gemma":[0.9946982,0.0006050065,0.001264017,0.001452167,0.001343961,0.0006366792],"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":[0.001183582,0.0006234631,0.01063191,0.002246947,0.00004596257,0.00008597653,0.1296845,0.8059142,0.0004876166,0.003002078,0.01774236,0.02835139],"study_design_scores_gemma":[0.00202326,0.0003087614,0.002452101,0.00115238,0.0002001648,0.00001628616,0.07348868,0.8004021,0.0000725303,0.0000263971,0.1184523,0.001405024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"review","genre_gemma":"other","genre_scores_codex":[0.2772346,0.3006443,0.08384807,0.06196414,0.0510993,0.03086148,0.001160477,0.002662886,0.1905248],"genre_scores_gemma":[0.09364745,0.01645159,0.1129615,0.005003733,0.001913675,0.003414286,0.02570016,0.001073522,0.7398341],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.5493093,"threshold_uncertainty_score":0.9997425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04433965299030817,"score_gpt":0.4334014850862734,"score_spread":0.3890618320959652,"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."}}