{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005209009,0.0003824466,0.0002266778,0.0004114323,0.0001420773,0.0003198497,0.0001838817,0.000255029,0.002653506],"category_scores_gemma":[0.001070387,0.0003382997,0.000159795,0.000138074,0.0003255401,0.0003862407,0.0002593122,0.0002407583,0.0004581659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001383674,"about_ca_system_score_gemma":0.0001102571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002939813,"about_ca_topic_score_gemma":0.0008809406,"domain_scores_codex":[0.9997343,0.00006693935,0.00002440179,0.00008754883,0.0000691668,0.00001758861],"domain_scores_gemma":[0.9994062,0.0002191995,0.0001514623,0.0001163074,0.00007748188,0.00002947181],"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":[0.0001208664,0.00001854377,0.00863189,0.0001054818,0.00001866744,0.00008860855,0.0000485319,0.0001841623,0.9848884,0.00003709654,0.00003766901,0.005820036],"study_design_scores_gemma":[0.0000243874,0.0008750434,0.4190628,0.00003026344,0.000108858,0.00496386,0.000134504,0.003833711,0.5688061,0.0001572991,0.001986018,0.00001726389],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802501,0.001387538,0.01674336,0.00005178719,0.00002367396,0.00002290282,0.00006763225,0.000181457,0.001271551],"genre_scores_gemma":[0.9820247,0.0004706189,0.01636647,0.00002341617,0.00001947462,0.00001352571,0.0001577904,0.00003650235,0.000887635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002653506,"threshold_uncertainty_score":0.00887686,"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."}}