The extent of the neurocognitive impairment in elderly survivors of war suffering from PTSD: meta-analysis and literature review
Bibliographic record
Abstract
OBJECTIVES: We performed a meta-analysis and systematic review on elderly survivors of war suffering from PTSD to estimate the variability in their cognitive impairment based on individual neuropsychological tests. METHODS: We included case control studies that explored the association of cognitive deficits in elderly PTSD civilian survivor of wars (age >60 years), using MEDLINE, Embase and PsycINFO from the inception to January 2018. We compared the cognitive performances in three comparisons i) PTSD+ vs. PTSD- civilian survivors of war; ii) PTSD+ vs. Control and iii) PTSD- vs. Control. The risk of bias was assessed using the Newcastle-Ottawa Scale for case-control studies. RESULTS: Out of 2939 titles and abstracts, 13 studies were eligible for data extraction. As compared to PTSD- civilian survivors of war, PTSD+ civilian survivors of war demonstrated significant deficits on TMT-A, TMT-B, Digit span backward, explicit memory low pair associate, CVLT recognition, WAIS-verbal and non-verbal tests. As compared to health controls, PTSD+ survivors demonstrated significantly lower performance on explicit memory low pair and high associate, RAVLT immediate and delayed recall, CVLT delayed and short cued recall. Performance on the neuropsychological test between PTSD- survivors of war and controls was not significant for all tests. CONCLUSION: The pattern suggests that PTSD+ survivors of war had poorer performance in tasks requiring processing speed, executive function, attention, working memory and learning. The magnitude of the cognitive deficits in our pooled analysis was small to moderate depending on the neuropsychological test. Most of our pooled analysis suffered from a high risk of bias, which lowered the confidence in our results.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".