{"id":"W2096267047","doi":"10.1007/s11095-014-1315-5","title":"Improved Utilization of ADAS-Cog Assessment Data Through Item Response Theory Based Pharmacometric Modeling","year":2014,"lang":"en","type":"article","venue":"Pharmaceutical Research","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; University of California, San Diego; National Institutes of Health; Genentech; IXICO; Canadian Institutes of Health Research; University of California, Los Angeles; Servier; Innovative Medicines Initiative; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; European Federation of Pharmaceutical Industries and Associations; Medpace; Eli Lilly and Company; Bristol-Myers Squibb; European Commission; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Synarc; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Cog; Item response theory; Medicine; Statistics; Computer science; Psychometrics; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0223806,0.0002203583,0.0004087868,0.0008850613,0.0002343833,0.00007508401,0.0007567082,0.0001120622,0.001923728],"category_scores_gemma":[0.009098822,0.0001846382,0.000107878,0.002244068,0.0003943266,0.0003462921,0.0008064746,0.001212457,0.00005680571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001840273,"about_ca_system_score_gemma":0.000909478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000301668,"about_ca_topic_score_gemma":6.923433e-7,"domain_scores_codex":[0.9913538,0.003595956,0.0005891388,0.0007867086,0.002640666,0.001033706],"domain_scores_gemma":[0.9911117,0.005851312,0.00006628188,0.00102706,0.001421828,0.0005218193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.04121931,0.005450308,0.02242811,0.002080575,0.0006493955,0.00009631729,0.0001734058,0.000144065,0.6344974,0.007943888,0.001405139,0.2839121],"study_design_scores_gemma":[0.005284893,0.0008894737,0.003682236,0.0001309655,0.0001766394,0.000005840244,0.0001438008,0.9064492,0.07312547,0.0009731289,0.008982723,0.0001556156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5567943,0.0009624453,0.420381,0.00450098,0.0001544308,0.002421974,0.00009209653,0.0001210596,0.01457173],"genre_scores_gemma":[0.9956784,0.0003353457,0.002340269,0.0006248056,0.0001466071,0.00009690251,0.0001931307,0.00005070698,0.0005337795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9063051,"threshold_uncertainty_score":0.999248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4919992571935844,"score_gpt":0.5981011077996339,"score_spread":0.1061018506060495,"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."}}