{"id":"W4396862807","doi":"10.1177/0272989x241249182","title":"The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations","year":2024,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canadian Partnership Against Cancer","funders":"National Cancer Institute; Erasmus Universitair Medisch Centrum Rotterdam; Health Canada; Cancer Australia; Partenariat Canadien Contre Le Cancer; Universität Zürich; Centre Hospitalier Universitaire Vaudois; ZonMw; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Institutes of Health; Cancer Research UK; Teva Pharmaceutical Industries","keywords":"Lung cancer; Medicine; Lung cancer screening; Cancer; Stage (stratigraphy); Incidence (geometry); Epidemiology; Internal medicine; Oncology; Demography; Biology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004846962,0.0001230722,0.0002163678,0.0001113175,0.0001951399,0.00005160893,0.0001061245,0.00007803764,0.001932057],"category_scores_gemma":[0.0004405451,0.00006308842,0.0002922693,0.0002551411,0.00007213478,0.00004763787,0.00004735266,0.0002677875,0.00001456344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004094511,"about_ca_system_score_gemma":0.0003898788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006840371,"about_ca_topic_score_gemma":0.00003448163,"domain_scores_codex":[0.9984423,0.00003865392,0.0003140366,0.0002365619,0.0007686481,0.0001998363],"domain_scores_gemma":[0.9980112,0.00147972,0.0000505203,0.000232318,0.00007325279,0.0001529677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002407946,0.0001639284,0.00534529,0.00004808016,0.0005398608,0.00004918662,0.0002861393,0.003138552,0.00002003179,0.003385702,0.09920995,0.8875725],"study_design_scores_gemma":[0.001056285,0.0001914706,0.01097486,0.008383891,0.0002432405,0.00002173332,0.0000466067,0.972146,0.00002012549,0.001188019,0.005633312,0.00009448931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4414187,0.03658348,0.4332136,0.07553835,0.002103226,0.001626025,0.0002817106,0.0003559884,0.008878917],"genre_scores_gemma":[0.9931628,0.00285848,0.002914237,0.0003346326,0.0001762383,0.0001102468,0.00001255169,0.00002228303,0.0004085349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9690074,"threshold_uncertainty_score":0.9989803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06180540773436391,"score_gpt":0.4747675484882615,"score_spread":0.4129621407538976,"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."}}