{"id":"W4406935563","doi":"10.1002/gepi.70001","title":"RetroFun‐RVS: A Retrospective Family‐Based Framework for Rare Variant Analysis Incorporating Functional Annotations","year":2025,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi; Université Laval","funders":"Alliance de recherche numérique du Canada","keywords":"Robustness (evolution); Computer science; Annotation; Computational biology; Biology; Data mining; Genetics; Artificial intelligence; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001880201,0.0003244724,0.0008808069,0.0003493181,0.0004110505,0.00001347707,0.0002945421,0.0007580762,0.00004475865],"category_scores_gemma":[0.0109946,0.000324006,0.0005606911,0.0009318277,0.0002273607,0.00000337162,0.0001246946,0.0002667963,0.00001022678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001065496,"about_ca_system_score_gemma":0.0003919348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007435958,"about_ca_topic_score_gemma":0.0001337169,"domain_scores_codex":[0.9962802,0.0008913644,0.001047399,0.001034802,0.00009718087,0.0006490647],"domain_scores_gemma":[0.9964907,0.001567488,0.000569816,0.0007661892,0.0004810626,0.0001247313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001707803,0.0001162991,0.8775122,0.00003274228,0.001950227,0.000002335793,0.00002596068,0.05589772,0.003848239,0.04090684,0.01734411,0.002192529],"study_design_scores_gemma":[0.0006530868,0.0003249139,0.8446713,0.00001648809,0.0006358154,0.00000338145,0.000105757,0.02712149,0.0001374547,0.1221267,0.003895112,0.0003085072],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1054014,0.001466168,0.8879868,0.003234443,0.0005715325,0.0004954777,0.0001961653,0.00003602925,0.0006119592],"genre_scores_gemma":[0.6305012,0.00007022442,0.3614072,0.005838381,0.0003437019,0.0004093838,0.0009611392,0.00002167873,0.0004471891],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5265797,"threshold_uncertainty_score":0.9999212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03118477260996855,"score_gpt":0.322256354886352,"score_spread":0.2910715822763835,"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."}}