Posttraumatic stress disorder symptom severity is associated with reduced Montreal Cognitive Assessment scores in a sample of Vietnam War Veterans
Bibliographic record
Abstract
Abstract The goal of the present study was to examine associations between posttraumatic stress disorder (PTSD) symptom severity, the number of stressors experienced, and cognitive outcomes in a sample of U.S. Vietnam War Veterans (N = 274). Adults between 60 and 85 years of age completed a Vietnam Veterans Alzheimer's Disease Neuroimaging Initiative Project visit. A modified version of the Life Stressor Checklist‐Revised (LSC‐R) was used to assess the number of stressful experiences participants experienced, current PTSD severity scores were measured via the Clinician‐Administered PTSD Scale for DSM‐IV (CAPS‐IV), and cognition was assessed using the Montreal Cognitive Assessment (MoCA). Linear regressions were conducted to examine the effect of CAPS‐IV and LSC‐R scores on cognitive performance. Higher CAPS‐IV scores were associated with worse cognitive outcomes on the MoCA, ΔF(1, 264) = 12.686, p < .001, R2 = .142. In contrast, the number of reported stressful experiences was not associated with cognitive outcomes. After accounting for multiple comparisons, findings indicated that CAPS‐IV severity scores were significantly associated with the MoCA memory index. In a sample of older Veterans, PTSD symptom severity, but not the number of reported stressors, was associated with poorer performance on a well‐established cognitive function screening tool. Analyses of specific MoCA domains indicated that memory may be driving this association. These findings suggest that highly arousing stressors characteristic of PTSD, rather than stressful experiences more broadly, contribute to this association. Future work can use these findings to explore whether treating PTSD symptoms may help maintain cognitive function during the aging process.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".