{"id":"W2591501810","doi":"10.1016/j.clon.2017.01.039","title":"Reducing Patient Waiting Times for Radiation Therapy and Improving the Treatment Planning Process: a Discrete-event Simulation Model (Radiation Treatment Planning)","year":2017,"lang":"en","type":"article","venue":"Clinical Oncology","topic":"Advances in Oncology and Radiotherapy","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; London Health Sciences Centre; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Radiation oncologist; Medicine; Radiation treatment planning; Bottleneck; Referral; Radiation therapy; Discrete event simulation; Medical physics; Waiting list; Process (computing); Operations management; Simulation; Computer science; Surgery; Family medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005206495,0.0002199914,0.0005727326,0.00004840897,0.0009423569,0.00003448602,0.00009430247,0.0003088662,0.000006911968],"category_scores_gemma":[0.0006905503,0.0001313343,0.0001666051,0.00002672423,0.0002603583,0.0001695904,0.00001574659,0.0001774211,0.000001122722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005553267,"about_ca_system_score_gemma":0.0003827316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001798261,"about_ca_topic_score_gemma":0.000003140475,"domain_scores_codex":[0.9983546,0.0001356377,0.0006614251,0.0004551685,0.0001067555,0.00028647],"domain_scores_gemma":[0.9964865,0.002105526,0.0008636258,0.0003553139,0.00007261795,0.000116404],"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":[0.002016582,0.0005537688,0.02437375,0.00001462577,0.000206022,0.00001101217,0.004653466,0.09455255,0.00008366812,0.00005680151,0.00003843092,0.8734393],"study_design_scores_gemma":[0.009211982,0.01784222,0.0227801,0.00007161297,0.0002226767,0.00001545326,0.000476856,0.9319302,0.0003171822,0.0007057044,0.01625293,0.0001730899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774186,0.00328263,0.01321386,0.003049551,0.0006739228,0.001940178,0.000012759,0.00005285027,0.000355633],"genre_scores_gemma":[0.9927049,0.001652776,0.003039871,0.0006669874,0.001238141,0.0003786653,0.00005378309,0.00002977376,0.0002351043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8732662,"threshold_uncertainty_score":0.7247943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.120057169617156,"score_gpt":0.5188478155286599,"score_spread":0.3987906459115039,"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."}}