The Canadian longitudinal study on aging as a platform for exploring cognition in an aging population
Bibliographic record
Abstract
Objective: We present descriptive information on the cognitive measures used in the Canadian Longitudinal Study on Aging (CLSA) Comprehensive Cohort, relate this to information on these measures in the extant literature, and identify key considerations for their use in research and clinical practice. Method: The CLSA Comprehensive Cohort is composed of 30,097 participants aged 45–85 years at baseline who provided a broad range of sociodemographic, physical, social, and psychological health information via questionnaire and took part in detailed physical and cognitive assessments. Cognitive measures included: the Rey Auditory Verbal Learning Test – immediate and 5-min delayed recall, Animal Fluency, Mental Alternation Test (MAT), Controlled Oral Word Association Test (COWAT), Stroop Test – Victoria Version, Miami Prospective Memory Test (MPMT), and a Choice Reaction Time (CRT) task. Results: CLSA Comprehensive Cohort sample sizes were far larger than previous studies, and performances on the cognitive measures were similar to comparable groups. Within the CLSA Comprehensive Cohort, main effects of age were observed for all cognitive measures, and main effects of language were observed for all measures except the CRT. Interaction effects (language × age) were observed for the MAT, MPMT Event-based score, all time scores on the Stroop Test, and most COWAT scores. Main effects of education were observed for all measures except for the MPMT Time score in the French sample, and interaction effects (age × education) were observed for the RAVLT (immediate and delayed) for the English sample and the Stroop Dot time for the French sample. Conclusion: This examination of the cognitive measures used in the CLSA Comprehensive Cohort lends support to their use in large studies of health and aging. We propose further exploration of the cognitive measures within the CLSA to make this information relevant to and available for clinical practice.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".