{"id":"W2130674512","doi":"10.1002/widm.1131","title":"Biomedical informatics and panomics for evidence‐based radiation therapy","year":2014,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Informatics; Translational bioinformatics; Radiation therapy; Health informatics; Computer science; Medical physics; Data science; Personalized medicine; Precision medicine; Interface (matter); Bioinformatics; Translational research informatics; Radiation oncology; Systems biology; Health informatics tools; Medicine; Genomics; Engineering informatics; Pathology; Biology; Genome; Internal medicine","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":[0.002286842,0.000242911,0.0006304909,0.0001501006,0.0002182206,0.0001587796,0.0002640952,0.00009847095,0.000007309739],"category_scores_gemma":[0.001296916,0.0001728169,0.00008963044,0.0001319065,0.0002664884,0.0007753019,0.0005737076,0.0002188031,0.00000643852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003227427,"about_ca_system_score_gemma":0.0001160662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000195924,"about_ca_topic_score_gemma":0.000001650343,"domain_scores_codex":[0.9984014,0.0001339814,0.0006844379,0.0003887325,0.0001284065,0.0002630275],"domain_scores_gemma":[0.9980794,0.0007522929,0.0002361118,0.0006605355,0.00004165369,0.0002300454],"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":[0.0001765731,0.0001088492,0.004737082,0.001146118,0.00004190946,0.000001283356,0.001379026,8.394742e-7,0.0002114253,0.00004822548,0.02298231,0.9691663],"study_design_scores_gemma":[0.002785076,0.001153341,0.002284017,0.008029062,0.0002484774,0.00009613898,0.0006428611,0.5405319,0.00002825786,0.00009207205,0.4437367,0.0003720712],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5933101,0.1807796,0.2144439,0.006535922,0.00138792,0.002267154,0.0002064713,0.0001299429,0.0009389977],"genre_scores_gemma":[0.7242819,0.1574061,0.1038966,0.004544274,0.003280942,0.0003147116,0.005238886,0.0001558325,0.0008808046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9687943,"threshold_uncertainty_score":0.7047267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08553935413831011,"score_gpt":0.3937327173282071,"score_spread":0.308193363189897,"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."}}