Exploring trends in brain fog and quality of life outcomes in Veterans with pain symptoms: A cross-sectional study
Bibliographic record
Abstract
Introduction: Brain fog may decrease quality of life (QOL) in Veterans with pain by reducing cognitive capacity and functional engagement. Veterans with higher pain interference (PI) experience worse QOL. Given PI's relationship with QOL, it is important to explore brain fog within the context of PI levels. This study compared health-related QOL, functional cognition, and functional abilities between Veterans with high versus low PI and brain fog. Method: A cross-sectional study, using a one-way MANOVA, explored whether Veterans with brain fog and pain symptoms perceived PI-affected measures of QOL. Exploratory post hoc testing compared gender differences and correlations between perceived confidence with abilities and QOL. Results: Thirty-four Veterans participated. The results of the MANOVA showed a significant main effect of PI between constructs. Univariate tests revealed differences in physical health, symptoms of mental health issues, and perceived confidence with abilities. No differences in outcomes based on gender were found. The exploratory correlation analysis demonstrates that in Veterans with brain fog and painful symptoms, there are high correlations between physical health and perceived confidence with abilities and moderate correlations between mental health and perceived confidence with abilities, and between functional cognition and perceived confidence with abilities. Discussion: Veterans with pain symptoms and brain fog with high PI demonstrated more mental health symptoms, poorer physical functioning, and reduced perceived abilities. Results from the correlation analysis demonstrate that perceived confidence plays a role in QOL. This study contributes to the overall understanding of this experience, identifying evidence-based suggestions for future exploration.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".