{"id":"W4406300138","doi":"10.1503/cmaj.241820","title":"Correction to “Screening for lung cancer”","year":2025,"lang":"en","type":"erratum","venue":"Canadian Medical Association Journal","topic":"Lung Cancer Treatments and Mutations","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data science; Lung cancer; World Wide Web; Information retrieval; Text mining; Medicine; Pathology; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01757786,0.003389068,0.003923316,0.005696117,0.005117981,0.006481698,0.004650623,0.01504562,0.08486769],"category_scores_gemma":[0.1900036,0.001910649,0.003982043,0.004325296,0.004023617,0.003878774,0.003613214,0.02673055,0.05636142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006699366,"about_ca_system_score_gemma":0.01555114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03097086,"about_ca_topic_score_gemma":0.03275516,"domain_scores_codex":[0.9796211,0.005117311,0.004477642,0.002583588,0.006667902,0.001532459],"domain_scores_gemma":[0.8994965,0.0356529,0.008425145,0.004527513,0.04746637,0.004431654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001371403,0.000001843152,0.00001923756,0.0001033575,0.000006125272,0.00004008405,0.00001508327,0.000006536113,0.000005084226,0.0002039067,0.9986578,0.0009271523],"study_design_scores_gemma":[0.000173492,0.00003023194,0.0007970553,0.001884942,0.00009084718,0.0003469017,0.0001006403,0.000145,0.0001122081,0.002098289,0.9941586,0.00006192315],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.00003446961,0.0006334543,0.000294444,0.1461419,0.8487554,0.00003790041,0.002354819,0.000276849,0.001470714],"genre_scores_gemma":[0.003361677,0.003464892,0.002471071,0.5121065,0.422273,0.0007041913,0.003115053,0.001057758,0.0514459],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.08486769,"threshold_uncertainty_score":0.2839107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008998456006819993,"score_gpt":0.3334731002788874,"score_spread":0.3244746442720675,"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."}}