{"id":"W4402561908","doi":"10.1016/j.annonc.2024.08.075","title":"67O Polygenic risk score associations with immune checkpoint inhibitors related endocrine toxicity","year":2024,"lang":"en","type":"article","venue":"Annals of Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"National Cancer Institute; Legend Biotech; Genentech; Esperion Therapeutics; EMD Serono; Centre Hospitalier Universitaire Vaudois; Seagen; Incyte; Institut des maladies génétiques Imagine; BioCryst; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Novavax; Regeneron Pharmaceuticals; Daiichi Sankyo Europe; Sanofi; National Science Foundation; Les Laboratories Pierre Fabre; AstraZeneca; Eli Lilly and Company; Amgen; Université de Lausanne; Pfizer","keywords":"Medicine; Polygenic risk score; Endocrine system; Toxicity; Oncology; Internal medicine; Bioinformatics; Genetics; Gene; Hormone; Single-nucleotide polymorphism; Genotype; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005220205,0.0004568454,0.0002604029,0.0008665745,0.0004205297,0.0007382062,0.0002875622,0.0005149316,0.008844792],"category_scores_gemma":[0.001956235,0.0001906222,0.001460546,0.001256595,0.0002493428,0.0003009734,0.0005021374,0.0007489282,0.0005794847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002584762,"about_ca_system_score_gemma":0.0004759868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006240798,"about_ca_topic_score_gemma":0.006589801,"domain_scores_codex":[0.9994789,0.0001669969,0.00005250824,0.0001107825,0.00006502175,0.0001257585],"domain_scores_gemma":[0.9986461,0.0004145,0.0005072911,0.0001262539,0.000103839,0.0002021223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001298398,0.00008255651,0.9789313,0.00004021136,0.001443459,0.0006424433,0.00005278465,0.0007924289,0.004245528,0.0004762685,0.0008003213,0.01119438],"study_design_scores_gemma":[0.00004153514,0.0002418899,0.9905875,0.00002588331,0.001047266,0.001191868,0.00007630963,0.003337222,0.0007980054,0.0007906894,0.001845542,0.000016319],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991214,0.001383639,0.001947281,0.0005423743,0.00008689345,0.00001221287,0.001577377,0.00005494248,0.003181398],"genre_scores_gemma":[0.9976185,0.0001531145,0.0002749222,0.00006333481,0.00004770561,0.000006289545,0.0004121161,0.00001235838,0.001411632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008844792,"threshold_uncertainty_score":0.02958876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02769848661028565,"score_gpt":0.3549313523673561,"score_spread":0.3272328657570704,"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."}}