Cognitive impairment indicator for the neuropsychological test batteries in the Canadian Longitudinal Study on Aging: definition and evidence for validity
Bibliographic record
Abstract
BACKGROUND: Prevalence of overall cognitive impairment based on each participant's performance across a neuropsychological battery is challenging; consequently, we define and validate a dichotomous cognitive impairment/no cognitive indicator (CII) using a neuropsychological battery administered in a population-based study. This CII approximates the clinical practice of interpretation across a neuropsychological battery and can be applied to any neuropsychological dataset. METHODS: Using data from participants aged 45-85 in the Canadian Longitudinal Study on Aging receiving a telephone-administered neuropsychological battery (Tracking, N = 21,241) or a longer in-person battery (Comprehensive, N = 30,097), impairment was determined for each neuropsychological test based on comparison with normative data. We adjusted for the joint probability of abnormally low scores on multiple neuropsychological tests using baserates of low scores demonstrated in the normative samples and created a dichotomous CII (i.e., cognitive impairment vs no cognitive impairment). Convergent and discriminant validity of the CII were assessed with logistic regression analyses. RESULTS: Using the CII, the prevalence of cognitive impairment was 4.3% in the Tracking and 5.0% in the Comprehensive cohorts. The CII demonstrated strong convergent and discriminant validity. CONCLUSIONS: The approach for the CII is a feasible method to identify participants who demonstrate cognitive impairment on a battery of tests. These methods can be applied in other epidemiological studies that use neuropsychological batteries.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".