{"id":"W4399660733","doi":"10.1016/j.ijrobp.2024.02.054","title":"Adapt or Perish: Adaptive RT for NSCLC","year":2024,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001953995,0.000396444,0.0002607951,0.0002113474,0.0001689604,0.0004445536,0.0004020697,0.0003240989,0.003855135],"category_scores_gemma":[0.0003947758,0.0001655181,0.0004087361,0.000174812,0.0002355049,0.0004493825,0.0006194079,0.0006004717,0.0005949232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001790807,"about_ca_system_score_gemma":0.0002427873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007156102,"about_ca_topic_score_gemma":0.001449251,"domain_scores_codex":[0.9999399,0.000007559232,0.000003244733,0.00001656078,0.00002043515,0.00001238697],"domain_scores_gemma":[0.9999484,0.00002080049,0.000008135807,0.000008938342,0.000003772696,0.00001000886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005046087,0.0005444073,0.01005292,0.0008592848,0.0004429106,0.000502057,0.0002676782,0.03149724,0.151687,0.002417774,0.007602905,0.7890798],"study_design_scores_gemma":[0.00344111,0.02143514,0.1852994,0.0005469745,0.002622715,0.01506953,0.0006122075,0.2819352,0.2537549,0.02299237,0.2117961,0.0004943181],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6992801,0.02128189,0.2234095,0.00287569,0.00146859,0.0004763041,0.001022292,0.006855333,0.0433303],"genre_scores_gemma":[0.9639764,0.002356094,0.02325609,0.0007859013,0.0002278137,0.0001061326,0.0003240297,0.0008340057,0.008133539],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003855135,"threshold_uncertainty_score":0.01289672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04858764449380664,"score_gpt":0.3982853142099042,"score_spread":0.3496976697160975,"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."}}