{"id":"W4405393660","doi":"10.1093/icvts/ivae211","title":"Feasibility of computed tomography-derived surgical margin assessment in an <i>ex vivo</i> sublobar lung resection model","year":2024,"lang":"en","type":"article","venue":"Interdisciplinary CardioVascular and Thoracic Surgery","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; Toronto General Hospital; University Health Network","funders":"University Health Network Foundation; University of Toronto","keywords":"Margin (machine learning); Computed tomography; Medicine; Concordance; Resection; Radiology; Ex vivo; High-resolution computed tomography; Pathological; Tomography; Surgical margin; Resection margin; Lung; Nuclear medicine; Surgery; In vivo; Pathology; Computer science; 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":[0.001665158,0.0002960195,0.00105732,0.0004863449,0.0000783171,0.00006365217,0.00007090469,0.0001560206,0.00003116466],"category_scores_gemma":[0.000008973858,0.000244231,0.001376022,0.0006213742,0.0001415348,0.0002736186,0.0002274966,0.0003222343,0.000001269654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003897267,"about_ca_system_score_gemma":0.0002403417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007245485,"about_ca_topic_score_gemma":0.00004952206,"domain_scores_codex":[0.9974065,0.0003533967,0.0005825575,0.0007979199,0.0005297112,0.0003298468],"domain_scores_gemma":[0.9986734,0.0002587811,0.00005287264,0.0006871416,0.000118452,0.0002093774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002729553,0.002206484,0.9043195,0.005175361,0.005668158,0.005499917,0.001466274,0.004024907,0.0007738887,0.0001430112,0.0009952626,0.06699763],"study_design_scores_gemma":[0.003107472,0.0009471117,0.7009708,0.007409387,0.003466447,0.001696814,0.0009580365,0.2711993,0.006814327,0.001886168,0.000404492,0.001139626],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977711,0.01838862,0.001361425,0.0003417936,0.0004965787,0.000650265,0.00004914563,0.0001109192,0.0008903095],"genre_scores_gemma":[0.9985912,0.0006798203,0.0002957355,0.00001583496,0.0001514077,0.00009810005,0.0001053896,0.0000392164,0.0000232516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2671744,"threshold_uncertainty_score":0.9959452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03470900458263211,"score_gpt":0.3685106657594553,"score_spread":0.3338016611768232,"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."}}