{"id":"W2800366688","doi":"10.1002/mp.12930","title":"Knowledge‐based automated planning for oropharyngeal cancer","year":2018,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Work & Health; Princess Margaret Cancer Centre; Canada Research Chairs; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline (software); Radiation treatment planning; Computer science; Benchmarking; Inverse; Nuclear medicine; Data mining; Artificial intelligence; Mathematics; Radiation therapy; Medicine; Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007947505,0.0009878837,0.0006241079,0.0009598726,0.0003196693,0.0009490859,0.001247143,0.0008210118,0.003293284],"category_scores_gemma":[0.003050051,0.000602848,0.001108039,0.0008705897,0.0004100533,0.0006005075,0.0008489701,0.001039517,0.0008189666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001591685,"about_ca_system_score_gemma":0.002116772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02481544,"about_ca_topic_score_gemma":0.02630033,"domain_scores_codex":[0.999451,0.0001458924,0.00002856972,0.0001531034,0.0001840899,0.00003738609],"domain_scores_gemma":[0.9990706,0.0005962832,0.00006809242,0.0001062106,0.000133131,0.00002565345],"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":[0.0001486957,0.00009416584,0.003333217,0.0003228302,0.0001058881,0.0001954695,0.0001143692,0.7621015,0.003638226,0.001375397,0.007915182,0.2206551],"study_design_scores_gemma":[0.00002990556,0.00003510879,0.0007370624,0.00002962248,0.00003760607,0.0000817297,0.00002306119,0.9891475,0.00326801,0.003493562,0.003102925,0.0000138382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06593499,0.002355122,0.9107502,0.0006854705,0.00007115686,0.0004566527,0.00299556,0.01179939,0.004951499],"genre_scores_gemma":[0.5621143,0.0009171853,0.4257263,0.0004382244,0.00004643737,0.0004860267,0.006987933,0.001033836,0.002249734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02481544,"threshold_uncertainty_score":0.04934198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0254246419743094,"score_gpt":0.3767529520272005,"score_spread":0.3513283100528911,"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."}}