{"id":"W2793893733","doi":"10.1158/1538-7445.sabcs17-pd4-11","title":"Abstract PD4-11: Copy-number and targeted sequencing analyses to identify distinct prognostic groups: Implications for patient selection to targeted therapies amongst anti-endocrine therapy resistant early breast cancers","year":2018,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"","keywords":"Breast cancer; Medicine; Oncology; Copy number analysis; PTEN; Targeted therapy; Context (archaeology); CDKN2A; Cancer; Copy-number variation; Internal medicine; Hazard ratio; Bioinformatics; Biology; PI3K/AKT/mTOR pathway; Genetics; Gene; Confidence interval","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":[0.0008111668,0.0001585112,0.000283596,0.0008367407,0.0002064146,0.0004719154,0.0002875318,0.0003037204,0.0025735],"category_scores_gemma":[0.002076426,0.0001004468,0.000171303,0.0004859702,0.0002374428,0.0001822604,0.0002721175,0.0002611699,0.0002428179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002380898,"about_ca_system_score_gemma":0.0001384357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005819724,"about_ca_topic_score_gemma":0.0006861871,"domain_scores_codex":[0.9996196,0.0001116899,0.00003716224,0.0001049522,0.00008831054,0.00003826617],"domain_scores_gemma":[0.9990782,0.0004665861,0.0002268159,0.00006849603,0.00008012841,0.00007987861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003373754,0.00005833329,0.9445179,0.00003510432,0.00009160002,0.0003608008,0.00008103301,0.0008824883,0.03304974,0.0001332905,0.0003476159,0.01706835],"study_design_scores_gemma":[0.0000798484,0.0007315822,0.9775249,0.000007216533,0.000101817,0.002154753,0.000108518,0.005627624,0.011935,0.0004575102,0.001261205,0.00001011635],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980112,0.0001822419,0.0007322113,0.00003875871,0.000003210562,0.00001521468,0.0004993742,0.00001259706,0.0005051422],"genre_scores_gemma":[0.9985435,0.00002821187,0.0007165154,0.00001912218,0.000003838624,0.00001660421,0.0003679322,0.000004623038,0.0002997092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0025735,"threshold_uncertainty_score":0.008609176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06039617734441132,"score_gpt":0.4124373564085102,"score_spread":0.3520411790640989,"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."}}