{"id":"W2058959360","doi":"10.1038/nrc2466","title":"Can genes for mammographic density inform cancer aetiology?","year":2008,"lang":"en","type":"review","venue":"Nature reviews. Cancer","topic":"AI in cancer detection","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Cancer Foundation","funders":"National Cancer Institute","keywords":"Breast cancer; Gene; MAMMOGRAPHIC DENSITY; Genetic predisposition; Cancer; Biology; Bioinformatics; Stromal cell; Genetics; Medicine; Cancer research; Mammography","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0005864625,0.001107919,0.003675858,0.0005412571,0.0004110893,0.0001135259,0.002347231,0.002067927,0.00004883608],"category_scores_gemma":[0.0001547179,0.0008416119,0.001909846,0.002130621,0.0001798037,0.0003646596,0.0003806453,0.002167138,0.00003488177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001436156,"about_ca_system_score_gemma":0.001974012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003506313,"about_ca_topic_score_gemma":0.002455351,"domain_scores_codex":[0.9953645,0.0002824924,0.001375205,0.001530199,0.0005359379,0.0009117054],"domain_scores_gemma":[0.9956468,0.0002910214,0.001615609,0.001711534,0.0004860686,0.0002489727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003412072,0.00001091423,0.00001647565,0.01301401,0.0001818749,0.000004677338,0.0000347378,0.000002098379,2.497958e-7,0.0005098867,0.02936927,0.9568524],"study_design_scores_gemma":[0.0001814164,0.00005205099,0.0000116988,0.01139304,0.0004797099,0.0001519535,6.696407e-7,0.00003286596,0.000006518752,0.0001981332,0.9865948,0.0008971463],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[4.718555e-7,0.9830807,0.004706241,0.0004487122,0.006875971,0.004264866,0.0002252808,0.0002594655,0.0001383156],"genre_scores_gemma":[0.000001202254,0.9832355,0.004680913,0.001457415,0.002158899,0.007836268,0.00007272535,0.00009790454,0.0004591073],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9572255,"threshold_uncertainty_score":0.9994035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0557732485331422,"score_gpt":0.3803880188994403,"score_spread":0.3246147703662982,"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."}}