{"id":"W3012388037","doi":"10.3390/app10061988","title":"Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine?","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Colorectal and Anal Carcinomas","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Medicine; Radiomics; Magnetic resonance imaging; Positron emission tomography; Radiology; Medical physics; Oncology","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":[0.002139165,0.0005250493,0.00113107,0.001660009,0.00025104,0.001734717,0.0005833947,0.001394283,0.001482438],"category_scores_gemma":[0.003217811,0.0002073027,0.0006816345,0.001633845,0.001305344,0.002454946,0.0006884575,0.002578615,0.0006898193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009412563,"about_ca_system_score_gemma":0.001268287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001116507,"about_ca_topic_score_gemma":0.001391625,"domain_scores_codex":[0.9992417,0.0003595038,0.00008419658,0.0001011334,0.0001747393,0.00003873226],"domain_scores_gemma":[0.9977714,0.00168723,0.0001396868,0.00007342138,0.0002800091,0.00004838725],"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":[0.00009700143,0.00004593683,0.001724716,0.006693887,0.0002211235,0.0002517629,0.0002486625,0.001319828,0.001357773,0.01667811,0.01994145,0.9514197],"study_design_scores_gemma":[0.00002040167,0.0002441266,0.005839776,0.007512976,0.0003009474,0.002391235,0.0004551343,0.001620954,0.001831805,0.04167008,0.9380074,0.0001052263],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.0005313435,0.9891944,0.002443655,0.006339996,0.0004382239,0.00000552868,0.00002373563,0.00002288111,0.001000145],"genre_scores_gemma":[0.007894196,0.985212,0.002733977,0.001822603,0.001697007,0.00001414678,0.00004736218,0.00001261136,0.0005662148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002139165,"threshold_uncertainty_score":0.01131314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04384226305616106,"score_gpt":0.2606833116414168,"score_spread":0.2168410485852557,"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."}}