{"id":"W4288426910","doi":"10.3399/bjgp22x720437","title":"Polygenic risk scores: improving the prediction of future disease or added complexity?","year":2022,"lang":"en","type":"article","venue":"British Journal of General Practice","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Cancer Research","funders":"Imperial College London; Royal Marsden NHS Foundation Trust","keywords":"Medicine; Disease; Polygenic risk score; Computer science; Data science; Machine learning; Internal medicine","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":[0.01785048,0.001798246,0.003743108,0.002115154,0.00068633,0.003981975,0.001580628,0.002916949,0.004689372],"category_scores_gemma":[0.08578707,0.0008448585,0.001834614,0.003238579,0.001723013,0.005265268,0.002567751,0.00503462,0.0009557259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006448086,"about_ca_system_score_gemma":0.001991959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005895114,"about_ca_topic_score_gemma":0.01017528,"domain_scores_codex":[0.9898953,0.006599285,0.0009852718,0.0009435396,0.001272743,0.0003037616],"domain_scores_gemma":[0.9415334,0.04160547,0.005293545,0.005720538,0.003759485,0.002087518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00106048,0.0003291703,0.8173001,0.0005750009,0.002712758,0.0003544389,0.000539908,0.002729072,0.0004302732,0.004684453,0.006415477,0.1628688],"study_design_scores_gemma":[0.0005082999,0.001312694,0.8599751,0.001510044,0.004018732,0.001778915,0.0007183808,0.0302364,0.0004480344,0.08863901,0.01053384,0.0003205963],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"commentary","genre_scores_codex":[0.7604553,0.06014963,0.04361266,0.1154868,0.002524112,0.0001694118,0.003402474,0.0004953418,0.01370421],"genre_scores_gemma":[0.9547101,0.01325712,0.02180151,0.004205073,0.003444153,0.00005471808,0.001345179,0.00006423338,0.001117839],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.01785048,"threshold_uncertainty_score":0.09440351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174732277559825,"score_gpt":0.267460577975888,"score_spread":0.2499873502199055,"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."}}