Telephone Administration of the Mental Alternation Test: Sensitivity to Cognitive Decline and Practice Effects across Midlife and Late Life
Bibliographic record
Abstract
BACKGROUND: We evaluated the utility of the telephone-administered Mental Alternation Test (MAT, an oral variant of the Trail-Making Test) for remote assessment of cognitive functioning in older adults. We examined (1) the sensitivity of MAT scores to cognitive change across 4 age groups, (2) practice effects associated with repeat administration, and (3) the uniformity of practice effects across age groups. METHODS: Community-dwelling volunteers were recruited randomly and categorized as young-middle-aged (45-54 years; n = 51), middle-aged (55-64 years; n = 58), young-old (65-74 years; n = 43) or old-old (75-85 years; n = 43). The participants completed the MAT twice within 2 weeks. The data were analyzed using mixed ANOVA. RESULTS: We found an effect of age on MAT performance [F(3, 191) = 11.37; p < 0.001], with planned comparisons revealing significantly lower scores in the old-old (p < 0.05). The scores on the second MAT administration were significantly higher than on the first administration [F(1, 191) = 12.82; p < 0.001], but this practice effect did not differ across age groups. CONCLUSIONS: The MAT was sensitive to cognitive decline in older adulthood. Practice effects were measurable but uniform across the observed age cohorts. As a brief telephone-administered test, the MAT represents a promising measure of cognitive functioning in older adults that is feasible for use in large-scale epidemiological studies.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".