{"id":"W2559937725","doi":"10.1016/j.atherosclerosissup.2016.10.001","title":"Pooling and expanding registries of familial hypercholesterolaemia to assess gaps in care and improve disease management and outcomes: Rationale and design of the global EAS Familial Hypercholesterolaemia Studies Collaboration","year":2016,"lang":"en","type":"article","venue":"Atherosclerosis Supplements","topic":"Lipoproteins and Cardiovascular Health","field":"Medicine","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Pooling; Medicine; Disease; Computer science; Internal medicine; Artificial intelligence","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":[0.0004217647,0.0001932465,0.0004256173,0.00006366523,0.0001155913,0.00003406656,0.00006660404,0.00005474402,0.000001128039],"category_scores_gemma":[0.0001390959,0.0001227206,0.00004356338,0.0001490435,0.0001727811,0.0001806853,0.0002271853,0.00005411759,1.16897e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001875689,"about_ca_system_score_gemma":0.00007789615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001445373,"about_ca_topic_score_gemma":0.0001863471,"domain_scores_codex":[0.9984706,0.0001429223,0.0004031698,0.0003752061,0.0003975381,0.000210537],"domain_scores_gemma":[0.999229,0.00007440778,0.0001165599,0.0002985259,0.0001388933,0.0001426871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002984974,0.00004442143,0.9729382,0.0006597003,0.0001710104,0.000001590235,0.001911004,0.000004492475,0.01562942,0.00008573688,0.00002705157,0.008228817],"study_design_scores_gemma":[0.005118932,0.0002634311,0.9846613,0.0008249325,0.000215861,0.000002275921,0.0065243,0.000008828391,0.002065811,0.00008079429,0.0001084923,0.0001249795],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947858,0.001467115,0.0004820162,0.0005625526,0.00008824705,0.002225706,0.0003752583,0.000007792594,0.000005488786],"genre_scores_gemma":[0.9956764,0.002452523,0.001397571,0.0001951911,0.00003108763,0.0002011568,0.00001019521,0.00001476795,0.00002108326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01356361,"threshold_uncertainty_score":0.5004402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04636939765045045,"score_gpt":0.3229991045431135,"score_spread":0.276629706892663,"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."}}