{"id":"W2070754959","doi":"10.1002/1098-2272(200102)20:2<149::aid-gepi1>3.0.co;2-a","title":"Design considerations for association studies of candidate genes in families","year":2001,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mount Sinai Hospital; Montreal General Hospital; University of Toronto; Lunenfeld-Tanenbaum Research Institute","funders":"National Institute of General Medical Sciences; Health Canada; National Institutes of Health","keywords":"Sample size determination; Covariate; Statistics; Correlation; Proband; Trait; Family aggregation; Sample (material); Econometrics; Generalized linear model; Mathematics; Genetics; Biology; Medicine; Computer science; Population; Gene; Mutation; Environmental health","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":[0.002175867,0.0001153137,0.000582271,0.0001120609,0.0000470759,0.000001502311,0.00006589083,0.0001344239,0.00001395499],"category_scores_gemma":[0.04183948,0.0001040927,0.00004584479,0.00009125992,0.00008917095,0.00003263962,0.00003060092,0.00007048502,0.000001724445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001249144,"about_ca_system_score_gemma":0.00004988897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005446109,"about_ca_topic_score_gemma":0.0005219564,"domain_scores_codex":[0.9981316,0.0005544047,0.0007948739,0.0001894143,0.00004923353,0.0002805227],"domain_scores_gemma":[0.9672832,0.03186314,0.0004310066,0.0001897758,0.0002100839,0.00002273987],"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":[0.0001389562,0.0003202511,0.5378476,0.0004706702,0.0006753912,0.00001589738,0.005374509,0.02549786,0.02497002,0.3337012,0.04422507,0.02676261],"study_design_scores_gemma":[0.0002230168,0.0001358223,0.003795796,0.00002516992,0.00002914344,0.000005659798,0.0002242997,0.0008297968,0.00471734,0.9896389,0.0002787074,0.00009640964],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1768983,0.00239976,0.8179964,0.001559875,0.000109354,0.0008031731,0.00001300298,0.00008683913,0.0001333599],"genre_scores_gemma":[0.2048568,0.003773087,0.7905383,0.00033808,0.00003668924,0.000326442,0.000002627495,0.00001296671,0.000115082],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6559377,"threshold_uncertainty_score":0.9662315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4847477091923624,"score_gpt":0.4999049966129594,"score_spread":0.01515728742059702,"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."}}