{"id":"W2190134232","doi":"10.1111/his.12912","title":"Whole‐mount pathology of breast lumpectomy specimens improves detection of tumour margins and focality","year":2015,"lang":"en","type":"article","venue":"Histopathology","topic":"Breast Lesions and Carcinomas","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"Canadian Cancer Society Research Institute; Canadian Breast Cancer Research Alliance","keywords":"Lumpectomy; Margin (machine learning); Medicine; McNemar's test; Sampling (signal processing); Radiology; Pathology; Mastectomy; Breast cancer; Internal medicine; Computer science; Mathematics; Cancer","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003780434,0.000141092,0.0005063677,0.0001692403,0.00003088108,0.000001831274,0.00006133208,0.0001654886,0.00002155539],"category_scores_gemma":[0.00005512721,0.000124842,0.00007846654,0.000130153,0.0004500318,0.0000392923,0.00007729068,0.0001744208,0.000009896325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001261198,"about_ca_system_score_gemma":0.0001151286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001859016,"about_ca_topic_score_gemma":0.00005953281,"domain_scores_codex":[0.9988722,0.0001263534,0.0003637418,0.0002899882,0.0001374749,0.0002102409],"domain_scores_gemma":[0.9990838,0.00002633827,0.0001897042,0.0003237807,0.0002217087,0.0001546495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003038848,0.0001720234,0.003222582,0.00007755325,0.000004179361,0.0001847753,0.0004781997,3.081264e-7,0.9941874,0.0006410321,0.00005612426,0.0006719087],"study_design_scores_gemma":[0.001527902,0.0007365871,0.9839711,0.00002066395,0.00009780283,0.005053765,0.0003928241,0.00002555464,0.006211283,0.0004883874,0.001379659,0.00009446291],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963173,0.0008107241,0.0007743756,0.0004249465,0.0002494935,0.0001804022,0.00004013618,0.00002576229,0.001176844],"genre_scores_gemma":[0.9989398,0.00001409902,0.000588186,0.00008527144,0.0000991232,0.00001081477,0.00001037371,0.00001687181,0.0002355316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9879761,"threshold_uncertainty_score":0.5090908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649336010975637,"score_gpt":0.2367982827418653,"score_spread":0.2203049226321089,"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."}}