{"id":"W6926731008","doi":"10.25384/sage.25904561.v1","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":"Figshare","topic":"Prenatal Screening and Diagnostics","field":"Medicine","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":[0.001688861,0.001159475,0.001189803,0.003980747,0.0016559,0.004042431,0.002843437,0.002224078,0.9273578],"category_scores_gemma":[0.03354871,0.001233152,0.0009205144,0.004418607,0.00055133,0.003319274,0.002342043,0.0018115,0.6563153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002182187,"about_ca_system_score_gemma":0.003391474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03353476,"about_ca_topic_score_gemma":0.04743746,"domain_scores_codex":[0.9987214,0.0001350767,0.0002094203,0.0002362149,0.0004836066,0.0002142323],"domain_scores_gemma":[0.9777395,0.009302427,0.001220987,0.001467203,0.008349803,0.001920068],"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":[0.00004285573,0.00002093817,0.000468189,0.0001986588,0.000003866957,0.00001924574,0.00002024972,0.00002717255,0.00003923212,0.0001714151,0.9939592,0.005029061],"study_design_scores_gemma":[0.0004016469,0.0000508243,0.01101879,0.001176852,0.00003159315,0.0003459132,0.0003881344,0.0003040064,0.0006297952,0.002511933,0.9830605,0.00007995823],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004172789,0.0001928835,0.000833053,0.001470085,0.0007333062,0.000292341,0.9661517,0.003014692,0.02689474],"genre_scores_gemma":[0.008425906,0.0008668007,0.00624632,0.003540633,0.001233313,0.001734057,0.8242267,0.007634079,0.1460921],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9273578,"threshold_uncertainty_score":0.1036152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03412991544150831,"score_gpt":0.3045299327928549,"score_spread":0.2704000173513466,"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."}}