{"id":"W2949803117","doi":"10.1002/gepi.22184","title":"Constrained instruments and their application to Mendelian randomization with pleiotropy","year":2019,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Douglas Mental Health University Institute; McGill University; Jewish General Hospital; McGill University Health Centre","funders":"Canadian Institutes of Health Research; Compute Canada; National Institute on Aging; Alzheimer's Disease Neuroimaging Initiative","keywords":"Mendelian randomization; Pleiotropy; Instrumental variable; Causal inference; Inference; Covariate; Outcome (game theory); Selection (genetic algorithm); Alzheimer's Disease Neuroimaging Initiative; Phenotype; Disease; Econometrics; Dementia; Biology; Computer science; Genetics; Genetic variants; Machine learning; Artificial intelligence; Mathematics; Medicine; Gene; Internal medicine; Genotype","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04308773,0.001729087,0.002377205,0.002606064,0.001027453,0.002088661,0.0033895,0.002559572,0.005925053],"category_scores_gemma":[0.1580656,0.001415708,0.002522052,0.003652882,0.005270243,0.003290497,0.004536667,0.00398226,0.0007713851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327838,"about_ca_system_score_gemma":0.003669566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003775627,"about_ca_topic_score_gemma":0.002267247,"domain_scores_codex":[0.9691294,0.02541297,0.0006917714,0.001995725,0.002329704,0.0004404018],"domain_scores_gemma":[0.8976846,0.08866895,0.004483595,0.006888072,0.00170826,0.0005665349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002426969,0.00006104183,0.004178248,0.0002087376,0.0004880811,0.0003030094,0.0003588673,0.1233957,0.0005758228,0.8045437,0.001301864,0.06434233],"study_design_scores_gemma":[0.0002230098,0.000105314,0.0009982483,0.00008241674,0.00007818561,0.0001350658,0.00003403678,0.3609523,0.0005179262,0.63196,0.004838312,0.00007523771],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00211803,0.0001427318,0.9969584,0.0001423652,0.00003682998,0.00009168831,0.00005163369,0.0001439126,0.000314496],"genre_scores_gemma":[0.09328435,0.0005580598,0.9026709,0.0003318772,0.0001378388,0.001309933,0.0001986886,0.0001708592,0.001337618],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04308773,"threshold_uncertainty_score":0.2278724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008337208863740475,"score_gpt":0.2500104710717234,"score_spread":0.2416732622079829,"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."}}