{"id":"W2793780291","doi":"10.1002/mgg3.377","title":"Association of common candidate variants with vascular malformations and intracranial hemorrhage in hereditary hemorrhagic telangiectasia","year":2018,"lang":"en","type":"article","venue":"Molecular Genetics & Genomic Medicine","topic":"Vascular Anomalies and Treatments","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"National Center for Advancing Translational Sciences; National Institute of Neurological Disorders and Stroke; National Institutes of Health; Rare Diseases Clinical Research Network","keywords":"Telangiectasia; Medicine; Arteriovenous malformation; Phenotype; Intracerebral hemorrhage; ACVRL1; Pathology; Lung; Vascular malformation; Endoglin; Candidate gene; Intracranial Arteriovenous Malformations; Internal medicine; Gene; Biology; Genetics; Subarachnoid hemorrhage; Cerebral angiography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003132345,0.000215105,0.0005066772,0.000192496,0.00005176674,0.000007867433,0.00008751181,0.0001195061,0.0001094646],"category_scores_gemma":[0.00003850416,0.0001707145,0.00005760244,0.0002559831,0.0001434893,0.00003292942,0.00005305936,0.000142969,0.00001530943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001458581,"about_ca_system_score_gemma":0.00008561744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005178268,"about_ca_topic_score_gemma":0.0003382227,"domain_scores_codex":[0.9985525,0.00007473533,0.0004280691,0.0002740567,0.0003688427,0.0003018162],"domain_scores_gemma":[0.9991397,0.00002579848,0.0001703127,0.0003941724,0.0001229887,0.0001470359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001446381,0.00160164,0.7142783,0.001199137,0.004413908,0.008592478,0.007926937,0.0003519846,0.2089568,0.0003202724,0.0005564311,0.05035572],"study_design_scores_gemma":[0.02029098,0.004097647,0.9462262,0.0004302548,0.002549784,0.002034419,0.0006324711,0.01214948,0.009759543,0.0003545136,0.0009692762,0.0005054689],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99339,0.002461786,0.001729092,0.0008586192,0.0001102695,0.000525284,0.00001941913,0.00001745862,0.0008880434],"genre_scores_gemma":[0.9938504,0.0002753348,0.005048888,0.0003853789,0.0001604944,0.00001391467,0.0001435173,0.00003541922,0.00008669512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2319479,"threshold_uncertainty_score":0.6961536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004608652918598346,"score_gpt":0.224430275892361,"score_spread":0.2198216229737626,"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."}}