{"id":"W6907911181","doi":"10.25384/sage.25904561","title":"sj-docx-1-caj-10.1177_08465371241253254 – Supplemental material for Decoding the Prevalent High-Risk Breast Cancers: Demographics, Pathological, Imaging Insights, and Long-Term Outcome","year":2024,"lang":"en","type":"article","venue":"Sage Journals Data","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Decoding methods; Outcome (game theory); Breast imaging; Magnetic resonance imaging; Medical imaging","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001921567,0.001217242,0.001391152,0.004735715,0.001563723,0.004536878,0.002891244,0.002289625,0.9315447],"category_scores_gemma":[0.03447862,0.001458813,0.001026264,0.00593479,0.0005598214,0.003501344,0.002337384,0.00218552,0.6938187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002273008,"about_ca_system_score_gemma":0.003965964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02844415,"about_ca_topic_score_gemma":0.04508644,"domain_scores_codex":[0.9984179,0.0001506412,0.0002965733,0.0002556083,0.0006234591,0.0002559003],"domain_scores_gemma":[0.9715394,0.01177051,0.001626557,0.00197242,0.01074418,0.002346939],"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":[0.00005828484,0.00003171933,0.0004100458,0.0002685225,0.00000525024,0.00001340452,0.00001721031,0.00002647097,0.00005775757,0.0001489438,0.99462,0.004342467],"study_design_scores_gemma":[0.0006666908,0.00007889322,0.01156434,0.001154538,0.00004012675,0.0002251588,0.0003722184,0.0002959039,0.0008181248,0.002455444,0.9822392,0.00008927265],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002955324,0.00009951004,0.0004189006,0.0008373107,0.0004300901,0.0002279311,0.9786683,0.001963376,0.01705903],"genre_scores_gemma":[0.004957418,0.000462429,0.003579701,0.002225891,0.0007551271,0.001427168,0.8816725,0.005086804,0.09983291],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9315447,"threshold_uncertainty_score":0.09764314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03052949941401446,"score_gpt":0.3238826449439859,"score_spread":0.2933531455299715,"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."}}