{"id":"W2405420781","doi":"10.1158/1538-7445.sabcs15-p4-03-05","title":"Abstract P4-03-05: Wide-field optical coherence tomography (WF-OCT) for near real-time, point-of-care assessment of margin status in breast-conserving surgery specimens: Results of a feasibility study at a high-volume single-centre","year":2016,"lang":"en","type":"article","venue":"Cancer Research","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"","keywords":"Lumpectomy; Optical coherence tomography; Breast-conserving surgery; Medicine; Breast cancer; Margin (machine learning); Surgical margin; Nuclear medicine; Radiology; Mastectomy; Cancer; Surgery; Computer science; Resection","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003360759,0.000545788,0.0003713757,0.0003589982,0.000445854,0.0004932292,0.001059305,0.0006512949,0.002602554],"category_scores_gemma":[0.001388319,0.0002809852,0.0003928935,0.0002050917,0.0005385317,0.0005014177,0.0007215401,0.0005722659,0.0008225659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005270492,"about_ca_system_score_gemma":0.001211741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002628095,"about_ca_topic_score_gemma":0.004024538,"domain_scores_codex":[0.9983064,0.000712363,0.00005623195,0.0002878596,0.0004386816,0.0001984078],"domain_scores_gemma":[0.9982752,0.0002909009,0.0001491929,0.0002248641,0.0005625648,0.0004973314],"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":[0.01084109,0.008441973,0.04371571,0.0003356165,0.0001372483,0.001426292,0.0006496182,0.002145746,0.8753319,0.0001636418,0.001818262,0.05499287],"study_design_scores_gemma":[0.003658501,0.1525399,0.4196768,0.00005854394,0.000440896,0.008461912,0.001224301,0.02116968,0.3856322,0.000245052,0.006727252,0.0001651039],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838647,0.0001214658,0.01307277,0.00007932209,0.00002240147,0.001682751,0.0002987461,0.0001955406,0.0006622779],"genre_scores_gemma":[0.9455363,0.00008533153,0.05143733,0.0001303019,0.00004426756,0.001136807,0.0007287404,0.00008838044,0.000812478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003360759,"threshold_uncertainty_score":0.01777363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05090446393164681,"score_gpt":0.3468311960753269,"score_spread":0.2959267321436801,"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."}}