{"id":"W2732106748","doi":"10.1093/geroni/igx004.4651","title":"INTEGRATIVE ANALYSIS OF LONGITUDINAL STUDIES ON AGING AND DEMENTIA (IALSA)","year":2017,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Construct (python library); Dementia; Longitudinal study; Harmonization; Selection (genetic algorithm); Longitudinal data; Psychology; Gerontology; Data science; Econometrics; Computer science; Statistics; Medicine; Artificial intelligence; Data mining; Mathematics; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2223291,0.00182731,0.003305919,0.01840716,0.002608133,0.005700124,0.00252921,0.001097396,0.005471078],"category_scores_gemma":[0.2423747,0.001118781,0.008075467,0.01277354,0.002372073,0.002549179,0.01428177,0.00224984,0.0008400488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004125469,"about_ca_system_score_gemma":0.01902382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005732303,"about_ca_topic_score_gemma":0.01016392,"domain_scores_codex":[0.8190034,0.148811,0.01311474,0.007115122,0.01077448,0.00118118],"domain_scores_gemma":[0.7383494,0.1582354,0.02717565,0.04048468,0.03173616,0.004018572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007209676,0.0003208245,0.1866759,0.01431095,0.02447659,0.0003062346,0.00966594,0.004418674,0.001740297,0.05352131,0.04218868,0.6616537],"study_design_scores_gemma":[0.000601556,0.002751911,0.4706452,0.02259421,0.02469,0.0008703088,0.007687465,0.02583694,0.004027932,0.1910335,0.2487619,0.0004989481],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05769003,0.06177216,0.7894698,0.01753543,0.002926365,0.01605161,0.01933464,0.004512119,0.0307078],"genre_scores_gemma":[0.1864585,0.01392311,0.7552952,0.001995808,0.0009020482,0.03041024,0.008298008,0.0006159978,0.002100954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2223291,"threshold_uncertainty_score":0.9590067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1268769787280993,"score_gpt":0.4620403726548065,"score_spread":0.3351633939267072,"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."}}