Sleep Quality and Reexperiencing Symptoms of PTSD Are Associated With Current Pain in U.S. OEF/OIF/OND Veterans With and Without mTBIs
Bibliographic record
Abstract
Pain, a debilitating condition, is frequently reported by U.S. veterans returning from Afghanistan and Iraq. This study investigated how commonly reported clinical factors were associated with pain and whether these associations differed for individuals with a history of chronic pain. From the Boston metropolitan area, 171 veterans enrolled in the Veterans Affairs Center of Excellence were assessed for current posttraumatic stress disorder (PTSD) symptom severity, current mood and anxiety diagnoses, lifetime traumatic brain injury, combat experiences, sleep quality, and alcohol use. Hierarchical regression models were used to determine the association of these conditions with current pain. Average pain for the previous 30 days, assessed with the McGill Pain Questionnaire, was 30.07 out of 100 (SD = 25.43). Sleep quality, PTSD symptom severity, and alcohol use were significantly associated with pain (R(2) = .24), as were reexperiencing symptoms of PTSD (R(2) = .25). For participants with a history of chronic pain (n = 65), only PTSD symptoms were associated with pain (R(2) = .19). Current pain severity was associated with increased PTSD severity (notably, reexperiencing symptoms), poor sleep quality, and increased alcohol use. These data support the hypothesis that PTSD symptoms influence pain, but suggest that problems with sleep and alcohol use may exacerbate the relationship.
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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 | 0.002 |
| 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".