{"id":"W4320016403","doi":"10.1016/j.ctro.2023.100590","title":"Artificial intelligence to predict outcomes of head and neck radiotherapy","year":2023,"lang":"en","type":"article","venue":"Clinical and Translational Radiation Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Radiation therapy; Head and neck cancer; Medicine; Context (archaeology); Radiomics; Medical physics; Clinical trial; Head and neck; Radiology; Surgery; Pathology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006629229,0.00007152515,0.0003349792,0.0001201756,0.00004574534,0.000005816476,0.00003390112,0.0001058499,0.00005564325],"category_scores_gemma":[0.0005339423,0.00005881405,0.00006155428,0.0001834785,0.0002097854,0.00003202883,0.0000089829,0.0001910691,0.000008683524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000667592,"about_ca_system_score_gemma":0.00008942868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009692494,"about_ca_topic_score_gemma":0.000004097515,"domain_scores_codex":[0.9989516,0.00007288367,0.0005197898,0.0001980949,0.0001430479,0.0001145919],"domain_scores_gemma":[0.9985937,0.001048615,0.00006443109,0.00006611864,0.00003956002,0.0001876122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001484343,0.00007480667,0.4653022,0.00001400688,0.00004569495,0.000004783401,0.0001665936,0.0001705838,0.00005585503,0.006156555,0.0001271406,0.5277333],"study_design_scores_gemma":[0.0005403282,0.0006374565,0.9276789,0.00001735531,0.00003459348,0.00001251699,0.00001857276,0.04484874,0.00001635033,0.006442612,0.01969711,0.00005540135],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9273716,0.0002299401,0.01417499,0.05737308,0.0002650919,0.0002309103,0.00001016815,0.0000397644,0.0003045192],"genre_scores_gemma":[0.9895377,0.0007876626,0.007290574,0.002014036,0.0002496636,0.000009066118,0.0000336853,0.000009082062,0.00006848969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.527678,"threshold_uncertainty_score":0.2398367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06672294476985431,"score_gpt":0.4533446849290613,"score_spread":0.386621740159207,"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."}}