Brain Fog in Veterans with Pain Symptoms
Bibliographic record
Abstract
Veterans experience chronic pain at a rate twice higher than civilians. Brain fog (BF), a phenomenon of mental cloudiness associated with functional challenges in cognition, is one of the least studied symptoms of chronic pain. Pain interference, a construct of chronic pain, can limit participation with activities. Both BF and pain interference can disrupt quality of life (QoL) in Veterans by limiting health and meaningful participation. The relationship between BF and pain interference on QoL has not been investigated. Thus, the objective of this study is twofold: 1) understand the experience and 2) explore the impacts and the possible mitigation of pain interference of BF on the QoL, in Veterans with pain symptoms and BF. First, a qualitative descriptive method was employed using content and matrix analyses, to describe the impacts of brain fog on QoL in Veterans with BF and pain symptoms. The content analysis revealed the triggers, impacts, management strategies, and suggestions for healthcare professionals. The matrix analysis showed that women described difficulty managing BF with competing roles (e.g., motherly duties). Next, we conducted a cross-sectional study Veterans, exploring whether the perceived level of pain interference in Veterans with BF and pain symptoms affected measures of QoL. Results indicated Veterans with BF and high pain interference showed more mental health symptoms (p=0.003), and less perceived level of confidence with abilities (0.036) and physical health (p=0.003), than Veterans with BF and low pain interference. Post-hoc tests revealed no significant differences across gender. Next, to explore how QoL constructs we related, we performed an exploratory correlational analysis, revealing significant correlations between perceived level of confidence with abilities and 1) mental health (r=-0.48), 2) physical health (r=-0.44), and 3) functional cognition (-0.44). This study contributes to the overall knowledge of BF, guiding recommendations for the development of an assessment and research priorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".