{"id":"W2765301122","doi":"10.1016/j.oraloncology.2017.10.013","title":"Improving margin revision: Characterization of tumor bed margins in early oral tongue cancer","year":2017,"lang":"en","type":"article","venue":"Oral Oncology","topic":"Head and Neck Cancer Studies","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Ottawa Hospital","funders":"National Center for Advancing Translational Sciences; National Center for Research Resources; National Cancer Institute; Medical Center, University of Pittsburgh","keywords":"Tongue; Margin (machine learning); Medicine; Cancer; Oral Cancers; Pathology; Internal medicine; 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":[0.002369116,0.0003897237,0.0004368652,0.0006104192,0.0004103616,0.0009674702,0.0005373461,0.0004305594,0.001076409],"category_scores_gemma":[0.004999613,0.000183319,0.0005407216,0.0005972947,0.0003497664,0.00069902,0.0005571053,0.0004572686,0.0001372559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006946258,"about_ca_system_score_gemma":0.0007323191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00442473,"about_ca_topic_score_gemma":0.008748717,"domain_scores_codex":[0.999274,0.0002630668,0.00008278868,0.00007667077,0.0001894353,0.0001141116],"domain_scores_gemma":[0.9983701,0.0005938244,0.0004610876,0.0001448094,0.0003159066,0.0001143216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006000992,0.0004284352,0.8329758,0.0007149366,0.0002658835,0.0001661012,0.001280392,0.0007715474,0.008040644,0.0001448546,0.0004245485,0.1487859],"study_design_scores_gemma":[0.00006063346,0.003399594,0.9864112,0.0001422633,0.0005099818,0.0003025853,0.001458881,0.0009527065,0.004140527,0.0001586051,0.002441389,0.00002167353],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949486,0.003850281,0.00032999,0.00005745506,0.00002298317,0.00003223834,0.00007151489,0.000004344061,0.0006825589],"genre_scores_gemma":[0.9981128,0.0008902242,0.0004746008,0.00003455015,0.00002030827,0.00001588354,0.00007455057,0.000003297061,0.0003739057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00442473,"threshold_uncertainty_score":0.01252925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04079189089177314,"score_gpt":0.3641656499160849,"score_spread":0.3233737590243118,"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."}}