{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009039674,0.0001466205,0.0002213493,0.0000195056,0.0001326581,0.00001352971,0.0002022601,0.00005487247,0.0007116855],"category_scores_gemma":[0.00001072741,0.0001273297,0.00008024694,0.0001413759,0.0001851504,0.00007411236,0.00002480723,0.0001375482,0.00001096679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003940333,"about_ca_system_score_gemma":0.0002054256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003128146,"about_ca_topic_score_gemma":0.000001094051,"domain_scores_codex":[0.9991358,0.00001838755,0.0001565025,0.0002188849,0.0001841688,0.0002862407],"domain_scores_gemma":[0.99941,0.0001107324,0.00006576441,0.0001694103,0.0001068711,0.0001372107],"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":[0.0002067985,0.0010967,0.03163374,0.0001405237,0.0003563235,0.000005510281,0.001327492,0.0001245492,0.007274756,0.01353191,0.1748256,0.7694761],"study_design_scores_gemma":[0.0040867,0.0006179288,0.001139938,0.0004887054,0.00008567337,5.500625e-7,0.00003569601,0.451278,0.3690555,0.02225414,0.1499841,0.0009731229],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007178691,0.0001291094,0.9879921,0.0001947359,0.0003293915,0.0002510035,0.00004495887,0.0005108848,0.00336912],"genre_scores_gemma":[0.9851918,0.00000178605,0.008925072,0.0004870862,0.004836143,0.0002416756,0.00005236705,0.00005345326,0.0002106649],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.979067,"threshold_uncertainty_score":0.7792457,"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."}}