{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0126538,0.0008645469,0.001212638,0.00544891,0.0006435016,0.00674095,0.001901984,0.002842733,0.005138141],"category_scores_gemma":[0.02585529,0.0005387084,0.001193762,0.00840656,0.004406961,0.009034151,0.003979187,0.006028557,0.002041753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002761889,"about_ca_system_score_gemma":0.004318857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001158065,"about_ca_topic_score_gemma":0.000985426,"domain_scores_codex":[0.9940578,0.002951995,0.0005437644,0.0005955394,0.001728896,0.0001219595],"domain_scores_gemma":[0.9724355,0.02176651,0.001326931,0.002225666,0.00177189,0.0004734919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005278501,0.00007911515,0.002470484,0.003424725,0.0001626978,0.0001526415,0.0002751037,0.003860765,0.0008463419,0.4708263,0.03984476,0.4780043],"study_design_scores_gemma":[0.00001585002,0.00004890782,0.001937014,0.003260687,0.00006127684,0.0003458771,0.0003262747,0.01127109,0.0007895422,0.665331,0.3165689,0.00004352449],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003632848,0.3037127,0.5096236,0.1417298,0.003125804,0.0004183598,0.001755409,0.001341536,0.03465979],"genre_scores_gemma":[0.06828666,0.3438624,0.5556625,0.01795554,0.006737553,0.0008273524,0.002102726,0.0003689479,0.00419632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0126538,"threshold_uncertainty_score":0.06692046,"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."}}