{"id":"W2128625986","doi":"10.1038/nrclinonc.2012.196","title":"Predicting outcomes in radiation oncology—multifactorial decision support systems","year":2012,"lang":"en","type":"review","venue":"Nature Reviews Clinical Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":395,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Institute for Cancer Research","funders":"Interreg; National Cancer Institute; KWF Kankerbestrijding; Center for Translational Molecular Medicine; European Commission","keywords":"Medicine; Radiation oncology; Predictive modelling; Outcome (game theory); Decision support system; Medical physics; Clinical decision support system; Clinical Oncology; Population; Precision medicine; Oncology; Radiation therapy; Intensive care medicine; Internal medicine; Data mining; Pathology; Computer science; Machine learning; Cancer","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":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.01843094,0.0008688839,0.0134125,0.000551164,0.00009032139,0.00003512432,0.0005976621,0.007532115,0.0002117658],"category_scores_gemma":[0.0350837,0.000548896,0.002320226,0.0006166855,0.000229012,0.0001418858,0.0002552753,0.01427542,0.0005720609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001890931,"about_ca_system_score_gemma":0.002493347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003885888,"about_ca_topic_score_gemma":0.00002061413,"domain_scores_codex":[0.986084,0.004223312,0.006921728,0.00116208,0.0006830237,0.0009257977],"domain_scores_gemma":[0.9806278,0.01362514,0.003714861,0.0009755458,0.0001512276,0.0009054305],"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":[0.00007664805,0.0008324715,0.01330233,0.007846587,0.000245497,0.0002133069,0.00002588396,4.885273e-7,5.930992e-8,0.00006365402,0.00618862,0.9712045],"study_design_scores_gemma":[0.003083894,0.001059536,0.0009970727,0.01232457,0.003494933,0.0004349205,0.000005900073,0.0001446406,7.139799e-9,0.000007314676,0.9780357,0.0004115295],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00003322916,0.9651482,0.0001058244,0.0005049234,0.02710715,0.004314504,0.00002027202,0.0000951619,0.002670721],"genre_scores_gemma":[0.00004411971,0.9835861,0.003882127,0.001249001,0.009877093,0.0003450299,0.0004754431,0.0001267184,0.0004143255],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9718471,"threshold_uncertainty_score":0.9996963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1317209994531531,"score_gpt":0.5501465579063007,"score_spread":0.4184255584531477,"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."}}