Population Normative Data for Commonly Used Neuropsychological Measures in Older Nigerian Adults in the READD‐ ADSP study
Bibliographic record
Abstract
BACKGROUND: Diagnostic classification of neurocognitive disorders (e.g., Alzheimer's disease) relies heavily on norm-based neuropsychological tests. Classification is optimized by the use of population-specific norms to provide appropriate reference groups. For this study, we established key adjustments for commonly used US and African neuropsychological measures in the Recruitment and Retention for Alzheimer's Disease Diversity Genetic Cohorts in the Alzheimer's Disease Sequencing Project (READD-ADSP). METHOD: Our dataset was drawn from older adults residing in three urban sites in Nigeria (Ibadan, Lagos, and Zaria) who were enrolled in the READD-ADSP study. Eligible participants were adjudicated as non-cognitively impaired; had no impairment on the Clinical Dementia Rating (CDR); a score of >=7 on the Intervention for Dementia in Elderly Africans (IDEA) cognitive screen; no functional impairment; no neuropsychiatric concerns; and no major health concerns/diseases. The dataset consisted of 226 individuals (71.3±6.7 years old, 43% Male, 8.9±6.3 years of education, and 73% were literate). Linear regression analyses estimated the effect of age, sex, education, and literacy (yes/no) on select neuropsychological tests including: Montreal Cognitive Assessment (MoCA), CERAD Word List, Category Fluency (Animal Naming), Verbal Fluency, Multilingual Naming Test (MINT), Number Span, Stick Design (SDT), and IDEA Cognitive screen (IDEA). RESULT: values for the 12 regression models ranged between 0.32 (Verbal Fluency) and 0.05 (Stick Design Immediate); nine of the models were significant; three models failed to attain significance (CERAD (Immediate and Delay) and the IDEA. Years of education was the most common significant factor. Individuals with greater education performed better on the MoCA, Animal Naming, Verbal Fluency, MINT, Numbers Forward, and Numbers Backward. Literacy was significant for three tests (Vegetable Naming, Numbers Forward, and SDT-Delay. Age was significant for Animal Naming, MINT, SDT (Immediate and Delay). Finally, sex was significant for CERAD (Immediate, Vegetable Naming, and Numbers Backwards. CONCLUSION: This study is a first step in addressing the need to develop test norms in the Nigeria population. Not surprisingly, years of education was often the most robust factor that needed to be adjusted for in these countries. We can use the regressions/normative values to provide age, sex, education, and literacy adjusted Z-scores to clinical adjudicators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".