{"id":"W2319533240","doi":"10.1158/0008-5472.sabcs-09-6015","title":"Advancing Breast Cancer HER2 FISH Quality by Image Analysis.","year":2009,"lang":"en","type":"article","venue":"Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Breast cancer; Medicine; Pathology; Stage (stratigraphy); Cancer; Biology; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.004732107,0.0005482851,0.0004343859,0.002125645,0.0003104237,0.001061694,0.0008112874,0.0005724639,0.008843359],"category_scores_gemma":[0.003861105,0.0004650552,0.0003655355,0.001072842,0.0005015853,0.001062626,0.000806167,0.0006543198,0.002585402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009941582,"about_ca_system_score_gemma":0.000355819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0018488,"about_ca_topic_score_gemma":0.0036454,"domain_scores_codex":[0.9982734,0.0004340521,0.0002006381,0.0003238384,0.000649446,0.0001186748],"domain_scores_gemma":[0.9974728,0.0008173446,0.0003428693,0.000264821,0.001019951,0.00008219365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007065905,0.00008708129,0.01273354,0.0004487284,0.00006504614,0.00006203775,0.0001773739,0.0007654947,0.9221517,0.0005139396,0.001190524,0.06109789],"study_design_scores_gemma":[0.00006104893,0.0004700608,0.1236813,0.00007130377,0.0001662634,0.001251864,0.0001590149,0.04278196,0.8192751,0.0005415777,0.01145657,0.00008397664],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5533479,0.005330538,0.4122214,0.0004693147,0.0001492319,0.001114414,0.002889127,0.006137697,0.0183404],"genre_scores_gemma":[0.553346,0.001602957,0.4315891,0.0001754235,0.00003939092,0.0007149006,0.002814569,0.001258904,0.008458824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008843359,"threshold_uncertainty_score":0.02958399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02988549941776134,"score_gpt":0.474985907764336,"score_spread":0.4451004083465747,"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."}}