{"id":"W3039753466","doi":"10.1371/journal.pcbi.1008036","title":"Is mammography screening beneficial: An individual-based stochastic model for breast cancer incidence and mortality","year":2020,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Institutes of Health; University of Utah","keywords":"Breast cancer; Mammography; Medicine; Incidence (geometry); Breast cancer screening; Mammography screening; Population; Harm; Cancer; Oncology; Demography; Internal medicine; Environmental health; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001192592,0.0001559142,0.0002939373,0.00008053612,0.0001267852,0.00002193875,0.0001088985,0.00009899272,0.00004231259],"category_scores_gemma":[0.00005784313,0.000147942,0.00007571166,0.0001909416,0.0001294928,0.0001037783,0.00004459215,0.0001290778,0.000001246758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002166119,"about_ca_system_score_gemma":0.0002185433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001392657,"about_ca_topic_score_gemma":0.00003670607,"domain_scores_codex":[0.9988164,0.00003006531,0.0002540293,0.0004277792,0.0002349637,0.0002367928],"domain_scores_gemma":[0.9991542,0.0001378996,0.0001059955,0.00008823353,0.0002785046,0.000235172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000952351,0.00009733537,0.461999,0.0001553225,0.000311589,0.000005645043,0.0007063496,0.5241443,0.0008857514,0.001626258,0.0002309274,0.008885114],"study_design_scores_gemma":[0.0007742515,0.0002644696,0.1959072,0.00005831702,0.0001545173,0.000009007974,0.00004088663,0.7997517,0.00003347417,0.00287145,0.000005058313,0.0001296764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6477985,0.0002126404,0.3468548,0.003591954,0.00002003752,0.0003030898,0.00115969,0.00005362108,0.000005688765],"genre_scores_gemma":[0.9570916,0.000003822547,0.02901914,0.01328798,0.0001847883,0.00006759552,0.0003295202,0.0000146177,9.210783e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3178357,"threshold_uncertainty_score":0.60329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1850592205299399,"score_gpt":0.3859078803721028,"score_spread":0.2008486598421629,"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."}}