{"id":"W4297858114","doi":"10.5206/uwomj.v90i1.8615","title":"Improving Margins of Resection in Surgical Oncology with the Intelligent Surgical Knife","year":2022,"lang":"en","type":"article","venue":"University of Western Ontario Medical Journal","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Surgical oncology; Resection; Surgical resection; Surgical margin; Cauterization; Breast cancer; Ex vivo; Cancer; Surgery; In vivo; Oncology; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00377737,0.00009329984,0.0002965053,0.0001368201,0.0003209117,0.00002169869,0.0008832898,0.00006714535,0.01109527],"category_scores_gemma":[0.00004968425,0.00006766743,0.00006226944,0.0001840401,0.0005060284,0.0001288115,0.000516679,0.0009124554,0.000006975753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006033528,"about_ca_system_score_gemma":0.001116,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04174668,"about_ca_topic_score_gemma":0.1046343,"domain_scores_codex":[0.9971963,0.0008322322,0.0003103943,0.0002040109,0.001193976,0.0002631471],"domain_scores_gemma":[0.9989169,0.0002828395,0.0004050249,0.0001559856,0.00007398452,0.0001652562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0234553,0.00343642,0.6743255,0.000252229,0.0001382695,0.03776922,0.07686392,0.07404424,0.0456553,0.001194823,0.001178628,0.06168616],"study_design_scores_gemma":[0.01140067,0.01044873,0.1253972,0.0004292838,0.0001442077,0.02861199,0.0227613,0.006323329,0.004025883,0.0001764539,0.789427,0.000853938],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956383,0.00002029358,0.001190854,0.001626172,0.0003371681,0.00008826253,0.000003258582,0.000008377193,0.00108734],"genre_scores_gemma":[0.9985707,0.00001843735,0.0006175633,0.000034709,0.00006296735,3.014731e-7,0.000001401696,0.000005084646,0.0006888211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7882484,"threshold_uncertainty_score":0.9898087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01106033829855676,"score_gpt":0.2377701932630701,"score_spread":0.2267098549645133,"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."}}