Normative data for teleneuropsychological testing: Findings from a Canadian adult cohort
Bibliographic record
Abstract
OBJECTIVE: Use of teleneuropsychological services has greatly increased since the beginning of the COVID-19 pandemic. The present study aimed to create normative data for a neuropsychological test battery of diverse cognitive domains in a Canadian population. METHOD: = 145). Data were stratified by age group as follows: 19-34, 35-49, 50-64, 65-79. Linear bivariate regression in the entire sample and groups stratified by age was used to test the relationship between age and test scores. Test scores were converted to z-scores using the mean and standard deviation for that group, with z-scores then transformed into normative scores for each test. RESULTS: Age was a significant predictor of scores for all tests except for FAS, HVLT-R (Retention, Recognition), and Digit Span (Forwards, Backwards). After raw test scores were regressed onto age for each group, age was no longer a significant predictor for most test scores, with exceptions for each age group. CONCLUSIONS: This study created normative data for a diverse teleneuropsychological test battery in a Canadian population. Standard-ized scores generally fell within the average range, with the exception of TOPF and JLO scores, which may be explained by high educational attainment and virtual testing environment, respectively. The results suggest that the teleneuropsychological testing environment results in similar performance to in-person assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".