{"id":"W3167339504","doi":"10.3389/fonc.2021.650335","title":"Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic","year":2021,"lang":"en","type":"article","venue":"Frontiers in Oncology","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Random forest; Selection (genetic algorithm); Head and neck; Heuristic; Computer science; Radiation therapy; Medical physics; Medicine; Machine learning; Artificial intelligence; Radiology; Surgery","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":[0.0002163121,0.0001124642,0.0003080304,0.00006982248,0.00009523053,0.00001691377,0.00003062333,0.00009124671,0.000001787173],"category_scores_gemma":[0.000050995,0.0001156527,0.00004346431,0.0000879768,0.00005168046,0.0001104857,0.00001268802,0.0001122849,1.984442e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009633134,"about_ca_system_score_gemma":0.0001117352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003369846,"about_ca_topic_score_gemma":0.000008990734,"domain_scores_codex":[0.9990058,0.00008191568,0.0004233867,0.0002904341,0.00004411001,0.0001543705],"domain_scores_gemma":[0.9993054,0.0002116041,0.0002042518,0.00007408511,0.0001672042,0.00003746782],"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":[0.0006775224,0.0001742841,0.2078698,0.000007693034,0.0000801565,9.034123e-7,0.0004457741,0.05432326,0.0009339441,0.0005466683,0.0003052921,0.7346347],"study_design_scores_gemma":[0.003752237,0.0004380738,0.001402122,0.00002493827,0.00001779758,0.000004582801,0.0002147422,0.9815904,0.0004293434,0.006379378,0.005616973,0.0001293359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3025256,0.000580769,0.6962748,0.00006429426,0.0001845597,0.000336744,0.00000746042,0.00001377899,0.00001199736],"genre_scores_gemma":[0.3924251,0.0003110716,0.6068546,0.0001100585,0.0001853816,0.00007516763,0.00001809514,0.00001560802,0.000004872718],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9272672,"threshold_uncertainty_score":0.4716178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.103360713325352,"score_gpt":0.407180250367169,"score_spread":0.303819537041817,"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."}}