{"id":"W2592117673","doi":"10.1158/1538-7445.sabcs16-p2-06-01","title":"Abstract P2-06-01: Non-genetic risk factors improve accuracy of breast cancer risk assessment for women at high familial risk: Comparison of risk estimation models using the prospective family study cohort (ProF-SC)","year":2017,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Risks and Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"","keywords":"Medicine; Breast cancer; Estimation; Risk assessment; Prospective cohort study; Environmental health; Demography; Cancer; Oncology; Internal medicine; Computer science; Engineering","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":[0.02528399,0.0008106368,0.001041238,0.0005340261,0.0003221121,0.001497902,0.0009896448,0.001267315,0.01123822],"category_scores_gemma":[0.07054066,0.000448235,0.003598412,0.0005535307,0.000279909,0.0008384024,0.0007009052,0.001254603,0.002118422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004278506,"about_ca_system_score_gemma":0.001064674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007185541,"about_ca_topic_score_gemma":0.003583398,"domain_scores_codex":[0.9935442,0.005061318,0.0002668871,0.0006049905,0.0003878066,0.0001348255],"domain_scores_gemma":[0.9605489,0.03105704,0.001529609,0.003630915,0.002513309,0.0007202487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.07441808,0.00113896,0.6975185,0.0006810033,0.01016725,0.0003581845,0.0003712635,0.01590818,0.003146348,0.0009125787,0.03454541,0.1608343],"study_design_scores_gemma":[0.008770256,0.0102684,0.7500126,0.0003491887,0.009786985,0.00169453,0.000275492,0.2019705,0.005937984,0.002288342,0.008386378,0.0002594433],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9524828,0.002070875,0.02482442,0.005295288,0.001207853,0.0003479628,0.01084828,0.0008181745,0.002104382],"genre_scores_gemma":[0.9827452,0.0003850322,0.009735367,0.0005554138,0.0002513889,0.0001027082,0.004330498,0.0002005171,0.001693836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02528399,"threshold_uncertainty_score":0.1337161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09920281031481215,"score_gpt":0.4686659867370404,"score_spread":0.3694631764222282,"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."}}