Post‐traumatic stress disorder and dementia in veterans: A scoping literature review
Bibliographic record
Abstract
Emerging research has found an association between post-traumatic stress disorder (PTSD) and dementia in veterans, yet little is known about the nature of this association and how it is conceptualized in the literature. The purpose of this scoping review is to understand how the relationship between PTSD and dementia in veterans is recognized and described in the peer-reviewed literature. A scoping review was conducted using Arksey and O'Malley's (International Journal of Social Research Methodology, 8, 19, 2005) framework. Articles are included if participants were veterans with a focus on PTSD and dementia. A total of six databases (CINAHL, MEDLINE, EMBASE, PsycINFO, HealthSTAR, and PubMed) were searched along with the reference lists of eligible sources in September 2018. Thematic analysis was used to summarize the data. Thirty-six studies were included in this review. Three main themes emerged from the literature: (i) symptomatic expression of PTSD and dementia; (ii) aetiology underlying the relationship between PTSD and dementia; and (iii) implications of PTSD and dementia on healthcare providers, treatment, and resources. This study highlights the ongoing need to understand mechanisms underlying the association between PTSD and dementia, the need for definition of PTSD symptoms, and to sensitize healthcare providers to the presence of PTSD when caring for veterans with dementia.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".