{"id":"W2791979376","doi":"10.1002/ijc.31370","title":"MRI background parenchymal enhancement, breast density and serum hormones in postmenopausal women","year":2018,"lang":"en","type":"article","venue":"International Journal of Cancer","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"National Cancer Institute","keywords":"Breast cancer; Medicine; Hormone; Estrone; Estrogen; Magnetic resonance imaging; Endocrinology; Internal medicine; Parenchyma; Menopause; Breast MRI; Cancer; Physiology; Pathology; Mammography; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001693323,0.00008990804,0.0001723601,0.0002408337,0.00001962095,0.00005888791,0.0001050511,0.00002616239,0.000238067],"category_scores_gemma":[0.000009374983,0.00007317523,0.00004091448,0.0001078056,0.0001180389,0.0003834377,0.00003774922,0.000124655,0.000004270426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001829415,"about_ca_system_score_gemma":0.0001156345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006546847,"about_ca_topic_score_gemma":0.00004245269,"domain_scores_codex":[0.999083,0.00001209511,0.0002733427,0.0001040283,0.0003633664,0.0001641038],"domain_scores_gemma":[0.9992072,0.00001880997,0.0001559746,0.00005037891,0.0004515233,0.0001160889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002676143,0.0003492623,0.7977391,0.00002093278,0.0007504589,0.0004105656,0.0008468836,0.000002045252,0.01455743,0.0003126099,0.0007930137,0.1815415],"study_design_scores_gemma":[0.002165064,0.0005988631,0.9871867,0.0003003443,0.00003717815,0.002955551,0.0003095138,0.00003769769,0.00270539,0.0008070446,0.002795862,0.0001008383],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943644,0.0005904533,0.0001628532,0.002641952,0.0005893218,0.00004557179,0.00001736373,0.000004242083,0.001583843],"genre_scores_gemma":[0.997882,0.0002739984,0.000176073,0.0006725361,0.0008450496,0.000002780889,0.000001658451,0.000006997939,0.0001389096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1894475,"threshold_uncertainty_score":0.2984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009587837108130427,"score_gpt":0.3048778969075071,"score_spread":0.2952900597993766,"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."}}