{"id":"W2967703731","doi":"10.1093/jncics/pkz049","title":"Toronto Workshop on Late Recurrence in Estrogen Receptor-Positive Breast Cancer: Part 2: Approaches to Predict and Identify Late Recurrence, Research Directions","year":2019,"lang":"en","type":"article","venue":"JNCI Cancer Spectrum","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Queen's University; Statistics Canada; Health Sciences Centre; Lunenfeld-Tanenbaum Research Institute; Juravinski Cancer Centre; Sunnybrook Health Science Centre; Sinai Health System; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"","keywords":"Medicine; Immunosurveillance; Breast cancer; Disease; Clinical trial; Estrogen receptor; Observational study; Oncology; Estrogen; Cancer; Cancer recurrence; Internal medicine; Intensive care medicine","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":[0.007850948,0.001174386,0.0008581116,0.002300372,0.001290313,0.002530332,0.001921211,0.003264385,0.006946495],"category_scores_gemma":[0.005396612,0.0004846088,0.001293776,0.001608086,0.001738891,0.001423111,0.002012747,0.005576556,0.001772803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01316382,"about_ca_system_score_gemma":0.01766195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1151057,"about_ca_topic_score_gemma":0.2846229,"domain_scores_codex":[0.9984243,0.0005063759,0.0001314834,0.0001827694,0.0005250766,0.0002299583],"domain_scores_gemma":[0.993795,0.0008864899,0.0002571182,0.0001386213,0.003110663,0.001812149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001871801,0.00005695211,0.001713891,0.001172331,0.00005706259,0.0002664616,0.0003405085,0.0002757866,0.0009951298,0.00212167,0.8670673,0.1257458],"study_design_scores_gemma":[0.00003488697,0.0001189005,0.004850786,0.001997766,0.00009583178,0.0003585443,0.0003526411,0.0001335343,0.0004505836,0.001118239,0.9904502,0.00003803841],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.003106504,0.4975393,0.004475033,0.3637839,0.1099161,0.0006472623,0.001621148,0.0001390855,0.0187717],"genre_scores_gemma":[0.03252486,0.6959772,0.005931397,0.08266178,0.09376598,0.0006415571,0.002340532,0.000145686,0.08601093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1151057,"threshold_uncertainty_score":0.2288713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05125633339763451,"score_gpt":0.3302817345161769,"score_spread":0.2790254011185424,"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."}}