{"id":"W4391227558","doi":"10.1017/s0714980823000806","title":"Drug and Natural Health Product Data Collection and Curation in the Canadian Longitudinal Study on Aging","year":2024,"lang":"en","type":"article","venue":"Canadian Journal on Aging / La Revue canadienne du vieillissement","topic":"Complementary and Alternative Medicine Studies","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Dalhousie University; Western University; Université de Sherbrooke; McMaster University; McGill University; Impact","funders":"Institute of Aging; Canadian Institutes of Health Research; Government of Canada; Canada Foundation for Innovation; McMaster University","keywords":"Data curation; Longitudinal data; Drug; Data collection; Natural product; Natural (archaeology); Product (mathematics); Data science; Medicine; Gerontology; Computer science; History; Biology; Sociology; Pharmacology; Data mining; Social science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04935738,0.0009752659,0.0009688087,0.008856316,0.004193416,0.002888363,0.002936529,0.0007007398,0.001977428],"category_scores_gemma":[0.07745456,0.0007163371,0.001145284,0.01078033,0.001065451,0.0009226816,0.003063449,0.001242612,0.000539708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03753592,"about_ca_system_score_gemma":0.1964387,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9779937,"about_ca_topic_score_gemma":0.985476,"domain_scores_codex":[0.955486,0.01273452,0.004304967,0.003574282,0.02211216,0.001788092],"domain_scores_gemma":[0.9294781,0.0118305,0.006046008,0.005749141,0.0451277,0.001768498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007538125,0.0004144224,0.4989513,0.005521344,0.0009422033,0.0004469728,0.006900041,0.00420496,0.003427772,0.007199326,0.09239234,0.3788455],"study_design_scores_gemma":[0.0002509227,0.0003072095,0.7879222,0.002444999,0.0006311819,0.0002241439,0.002222863,0.005939204,0.00479638,0.001562515,0.1934586,0.0002398595],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3989109,0.0395443,0.1434209,0.03477212,0.001641411,0.04331017,0.2560101,0.002951222,0.07943888],"genre_scores_gemma":[0.5563026,0.01875437,0.315131,0.007992946,0.0004761907,0.01024655,0.08055799,0.0003642463,0.01017404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9779937,"threshold_uncertainty_score":0.2723435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05862962487190831,"score_gpt":0.3300426251956335,"score_spread":0.2714130003237252,"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."}}