A comparison of neuropsychological performance between US and Russia: Preparing for a global clinical trial
Bibliographic record
Abstract
BACKGROUND: Understanding regional differences in cognitive performance is important for interpretation of data from large multinational clinical trials. METHODS: Data from Durham and Cabarrus Counties in North Carolina, USA and Tomsk, Russia (n = 2972) were evaluated. The Montreal Cognitive Assessment (MoCA), Trail Making Test Part B (Trails B), Consortium to Establish a Registry for Alzheimer's Disease Word List Memory Test (WLM) delayed recall, and self-report Alzheimer's Disease Cooperative Studies Mail-In Cognitive Function Screening Instrument (MCFSI) were administered at each site. Multilevel modeling measured the variance explained by site and predictors of cognitive performance. RESULTS: Site differences accounted for 11% of the variation in the MoCA, 1.6% in Trails B, 1.7% in WLM, and 0.8% in MCFSI scores. Prior memory testing was significantly associated with WLM. Diabetes and stroke were significantly associated with Trails B and MCFSI. CONCLUSIONS: Sources of variation include cultural differences, health conditions, and exposure to test stimuli. Findings highlight the importance of local norms to interpret test performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".