{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002994938,0.001425633,0.001447392,0.001296316,0.001546993,0.001725276,0.00240117,0.001593244,0.00324444],"category_scores_gemma":[0.008125016,0.0009672813,0.002335177,0.001639281,0.001325255,0.0007010147,0.0008898434,0.001189876,0.000181501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04570279,"about_ca_system_score_gemma":0.02273178,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9630515,"about_ca_topic_score_gemma":0.9091225,"domain_scores_codex":[0.9987171,0.0004736645,0.0000423985,0.0001702966,0.0001450512,0.0004516467],"domain_scores_gemma":[0.9934241,0.004040916,0.000634436,0.0001815,0.001252199,0.0004668721],"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":[0.000215462,0.00008527423,0.01181365,0.00004622496,0.0001071045,0.00009831821,0.00005127022,0.983973,0.0001107347,0.001564003,0.0007028014,0.00123206],"study_design_scores_gemma":[0.0001281222,0.0001067098,0.01006126,0.0000224357,0.0001137544,0.00001965071,0.0001610832,0.9881439,0.00008066832,0.0005983083,0.0005317832,0.00003230786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981587,0.0006912622,0.004547024,0.001117659,0.00003107654,0.0003040821,0.004422003,0.0000784369,0.007221424],"genre_scores_gemma":[0.9940872,0.0002977914,0.001781926,0.00008320258,0.000009171082,0.0001186809,0.00141789,0.00001271184,0.002191357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04570279,"threshold_uncertainty_score":0.3315985,"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."}}