{"id":"W3020909737","doi":"10.1101/2020.04.23.20077099","title":"Improving reporting standards for polygenic scores in risk prediction studies","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Diseases; National Human Genome Research Institute; Eunice Kennedy Shriver National Institute of Child Health and Human Development; NIHR Cambridge Biomedical Research Centre; Economic and Social Research Council; Medical Research Council; Chief Scientist Office, Scottish Government Health and Social Care Directorate; Cambridge University Hospitals; National Institutes of Health; University of Cambridge; Department of Health and Social Care; Scottish Government; British Heart Foundation; Canadian Institutes of Health Research; Health and Social Care Research and Development Division; National Institute for Health and Care Research; Public Health Agency; Engineering and Physical Sciences Research Council; European Molecular Biology Laboratory","keywords":"Benchmarking; Computer science; Population; Best practice; Data science; Medicine; Environmental health; Business","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002638319,0.0002198736,0.0005010598,0.00006163356,0.0001004043,0.00001630754,0.000171881,0.000382552,0.000002235882],"category_scores_gemma":[0.01768105,0.0002114692,0.0002156144,0.00006240833,0.00005485118,0.000001395603,0.0004933628,0.0003077697,6.559965e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009364943,"about_ca_system_score_gemma":0.0003480792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001197592,"about_ca_topic_score_gemma":0.0002935281,"domain_scores_codex":[0.9974988,0.0001726395,0.001195707,0.0006937862,0.0001397785,0.000299327],"domain_scores_gemma":[0.9974653,0.00006761352,0.00176666,0.0003712406,0.0002714183,0.00005773724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008966021,0.0000204329,0.9697894,0.0002524031,0.0002979302,0.000003223274,0.0002143194,0.002397663,0.02097632,0.000009468113,0.001827661,0.00412151],"study_design_scores_gemma":[0.001401712,0.0007190326,0.9596326,0.0002114526,0.0003381571,0.000009128609,0.0007370966,0.01101696,0.01121553,0.004978829,0.00899097,0.0007484937],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9694969,0.005470067,0.02272657,0.000422731,0.0007683565,0.0005255343,0.000512476,0.0000244145,0.00005291604],"genre_scores_gemma":[0.9911128,0.002356584,0.005120735,0.00009354766,0.0006669577,0.0002194538,0.000301388,0.00003353211,0.0000950258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02161584,"threshold_uncertainty_score":0.9905934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04761432952167163,"score_gpt":0.3483043677983723,"score_spread":0.3006900382767006,"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."}}