{"id":"W2966239700","doi":"10.1136/thoraxjnl-2019-213156","title":"Lung cancer screening: enhancing risk stratification and minimising harms by incorporating information from screening results","year":2019,"lang":"en","type":"letter","venue":"Thorax","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Lung cancer screening; Risk stratification; Cancer screening; Lung cancer; Risk assessment; Environmental health; Cancer; Intensive care medicine; Oncology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04114745,0.002710822,0.003967496,0.003019994,0.0005321553,0.002927792,0.002011097,0.004704995,0.006751516],"category_scores_gemma":[0.1137358,0.001244985,0.005015731,0.002246262,0.001410759,0.005378746,0.002108238,0.003272429,0.0006654933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00205022,"about_ca_system_score_gemma":0.003855025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003152765,"about_ca_topic_score_gemma":0.003265433,"domain_scores_codex":[0.9372357,0.05238854,0.002815644,0.002449073,0.004454295,0.0006566797],"domain_scores_gemma":[0.92012,0.07006208,0.005991595,0.001908176,0.001058002,0.0008600479],"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":[0.04738748,0.006448619,0.01359235,0.03131573,0.0116853,0.0001573027,0.001035789,0.02418106,0.001136046,0.01190997,0.006560395,0.84459],"study_design_scores_gemma":[0.1473657,0.09100954,0.1148104,0.07013287,0.0815945,0.001186739,0.0008198167,0.1885456,0.006179911,0.2477382,0.04900318,0.001613458],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"commentary","genre_scores_codex":[0.2959763,0.2827866,0.2864154,0.06465053,0.003055866,0.02357627,0.005623235,0.002197771,0.03571795],"genre_scores_gemma":[0.8115839,0.02931149,0.1371664,0.008589492,0.001993171,0.008190377,0.001210383,0.0001129593,0.001841666],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.04114745,"threshold_uncertainty_score":0.2176111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01819772677230509,"score_gpt":0.2949657583280433,"score_spread":0.2767680315557382,"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."}}