{"id":"W2587263164","doi":"10.1371/journal.pmed.1002225","title":"Performance and Cost-Effectiveness of Computed Tomography Lung Cancer Screening Scenarios in a Population-Based Setting: A Microsimulation Modeling Analysis in Ontario, Canada","year":2017,"lang":"en","type":"article","venue":"PLoS Medicine","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; Institute for Clinical Evaluative Sciences; Ontario Tobacco Research Unit; Public Health Ontario; University of Toronto; Ottawa Hospital; Brock University","funders":"Ontario Ministry of Health and Long-Term Care; National Cancer Institute; Institute for Clinical Evaluative Sciences; Cancer Care Ontario","keywords":"Lung cancer screening; Medicine; Overdiagnosis; Population; Microsimulation; Cost effectiveness; Quality-adjusted life year; Cancer registry; Cost–benefit analysis; Cancer screening; Lung cancer; Propensity score matching; Health care; Environmental health; Demography; Cancer; Surgery; Radiology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002924784,0.0001495134,0.0005910086,0.0004050999,0.0000985177,0.000009068453,0.00006219668,0.00004981403,0.00002556242],"category_scores_gemma":[0.00006022494,0.0001238501,0.00004170425,0.0003221173,0.00003626304,0.00007682834,0.00002031892,0.0001528818,1.658282e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009376801,"about_ca_system_score_gemma":0.0003854668,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.982476,"about_ca_topic_score_gemma":0.987482,"domain_scores_codex":[0.9988443,0.00004743218,0.0003519592,0.0002783759,0.0002922577,0.0001856723],"domain_scores_gemma":[0.999179,0.0001650697,0.0001897043,0.0002661544,0.000115429,0.00008465871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002537156,0.00008660268,0.8066113,0.0003483517,0.0002978821,0.00001318034,0.0002400258,0.1914675,0.0001142258,8.640378e-7,0.000004308266,0.0005620355],"study_design_scores_gemma":[0.002927267,0.00004490019,0.5196465,0.002779962,0.0004869611,3.184093e-7,0.00001278134,0.4739446,0.0001022902,6.170076e-7,9.837618e-7,0.00005285985],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978143,0.0004294186,0.0002412216,0.0005390212,0.00004710854,0.0008803892,0.000006615739,0.000007609628,0.00003430917],"genre_scores_gemma":[0.9993553,0.00002406761,0.0002458597,0.00009256618,0.00002368265,0.0001105735,0.0001328952,0.00001091504,0.000004130037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2869648,"threshold_uncertainty_score":0.505046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02597510090437717,"score_gpt":0.3017863628750315,"score_spread":0.2758112619706544,"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."}}