{"id":"W2192742520","doi":"10.1089/jwh.2015.5405","title":"Breast Cancer Screening in the Setting of Dense Breast Tissue","year":2015,"lang":"en","type":"article","venue":"Journal of Women s Health","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Health Services and Policy Research","funders":"American Cancer Society","keywords":"Breast cancer; Medicine; Breast cancer screening; Mammography; Library science; Cancer; Gerontology; Internal medicine; Computer science","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":[0.0007363762,0.0003171585,0.0004796489,0.001504408,0.0005463767,0.001162314,0.0004156481,0.0009434273,0.00258175],"category_scores_gemma":[0.008460758,0.0004595025,0.0002498578,0.0009249124,0.0005516139,0.0007283846,0.0008316031,0.0008176102,0.0002590155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003618132,"about_ca_system_score_gemma":0.0005457919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004639791,"about_ca_topic_score_gemma":0.008177086,"domain_scores_codex":[0.9991913,0.0002909483,0.00005689258,0.00008416585,0.0001037339,0.0002729065],"domain_scores_gemma":[0.9977394,0.001218218,0.000488807,0.00007125273,0.0001351607,0.0003471559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009422225,0.0001813179,0.971298,0.00007014856,0.00003992879,0.00847551,0.0002319673,0.0003708448,0.001465441,0.0002384494,0.0007957538,0.0158904],"study_design_scores_gemma":[0.00007631463,0.0009895707,0.9282264,0.00015738,0.0001900324,0.04744629,0.004926266,0.01233935,0.001564142,0.002642065,0.001405544,0.00003668026],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932499,0.001561259,0.0006796066,0.0009378133,0.00003832278,0.00002251093,0.0001617353,0.00001881051,0.003329998],"genre_scores_gemma":[0.9988651,0.0003842551,0.0003877876,0.00008243634,0.0000497683,0.000004139063,0.00006637764,0.000001955472,0.0001581314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004639791,"threshold_uncertainty_score":0.009225607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02921733542575878,"score_gpt":0.3259385738196833,"score_spread":0.2967212383939245,"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."}}