{"id":"W3122107622","doi":"10.1158/0008-5472.can-20-1237","title":"Assessing Lung Cancer Absolute Risk Trajectory Based on a Polygenic Risk Model","year":2021,"lang":"en","type":"article","venue":"Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Princess Margaret Cancer Centre; Lunenfeld-Tanenbaum Research Institute; Public Health Ontario; University of Toronto","funders":"National Center for Advancing Translational Sciences; Medical Research Council; Canadian Institutes of Health Research; Canadian Cancer Society Research Institute; National Institutes of Health; Medical Research and Materiel Command; Fundación para el Fomento en Asturias de la Investigación Científica Aplicada y la Tecnología; Princess Margaret Hospital Foundation; Universidad de Oviedo; Vanderbilt University; Moffitt Cancer Center; Herlev Hospital; Norges Forskningsråd; Vanderbilt University Medical Center; Cancer Care Ontario; National Center for Research Resources; Georgia Clinical and Translational Science Alliance; National Cancer Institute; Sundhed og Sygdom, Det Frie Forskningsråd; World Health Organization; Roy Castle Lung Cancer Foundation; U.S. Department of Defense","keywords":"Lung cancer; Absolute risk reduction; Cancer; Medicine; Oncology; Statistics; Internal medicine; Mathematics; Confidence interval","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01144308,0.0005633335,0.0008720638,0.001033747,0.000478409,0.001123525,0.0008983976,0.0007888686,0.00163238],"category_scores_gemma":[0.01749323,0.0003878795,0.001368499,0.0007646752,0.000781299,0.0006769129,0.0008963523,0.001212999,0.0002511681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000791943,"about_ca_system_score_gemma":0.0006950956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01484166,"about_ca_topic_score_gemma":0.006794503,"domain_scores_codex":[0.9972247,0.001943945,0.00007345237,0.0005115144,0.000111259,0.0001351478],"domain_scores_gemma":[0.9866381,0.01064182,0.0009858501,0.001014787,0.0004458312,0.0002736278],"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":[0.0006109725,0.0001368169,0.4768313,0.00004955486,0.0009714995,0.0003668966,0.0002396096,0.4990383,0.00119705,0.003762795,0.0007355088,0.01605989],"study_design_scores_gemma":[0.00001997402,0.0001430872,0.05293639,0.0000150866,0.0001234205,0.0001144278,0.00003548587,0.9429118,0.0001569878,0.0033424,0.0001807729,0.00002017659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9401748,0.0001811221,0.05820364,0.0003138997,0.00001145501,0.00004657501,0.0005718777,0.00009681437,0.0003998546],"genre_scores_gemma":[0.9942021,0.00003954368,0.004920659,0.00003284382,0.000005910244,0.00002481689,0.0004861004,0.000009451976,0.0002785387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01484166,"threshold_uncertainty_score":0.06051749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06646280740802088,"score_gpt":0.464872775981748,"score_spread":0.3984099685737271,"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."}}