Severity of Musculoskeletal Pain and Its Effect on Psychosocial Factors in Veterans With Posttraumatic Stress Disorder
Bibliographic record
Abstract
PURPOSE: The aim of this study is to investigate the relationship between psychosocial factors and the severity of musculoskeletal pain in veterans with posttraumatic stress disorder (PTSD). METHODS: A total of 60 subjects were recruited from among the veterans with musculoskeletal pain at D Veterans Hospital. PTSD was evaluated by using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; severity of pain was measured by using the short-form McGill Pain Questionnaire (SF-MPQ); depression and anxiety were measured by using the Symptom Checklist-90-Revision; and the quality of sleep was measured by using the Pittsburgh Sleep Quality Index. All data were analyzed using SPSS 18.0 software for Windows. RESULTS: The averages cores of pain intensity ( $7.48{\pm}1.67$ ), SF-MPQ-sensory ( $13.84{\pm}7.52$ ), SF-MPQ-affective ( $4.41{\pm}3.79$ ), depression ( $19.30{\pm}11.37$ ), anxiety ( $13.39{\pm}7.99$ ), and quality of sleep ( $10.05{\pm}5.89$ ) were obtained in veterans with PTSD. SF-MPQ-sensory measures sleep quality (r=0.346, p<0.01), SF-MPQ-affective measures depression (r=0.318, p<0.01) and anxiety (r=0.404, p<0.01), and these showed a statistically significant positive correlation in veterans with PTSD. Pain levels were observed to be higher in veterans with PTSD. Moreover, in these subjects, physical pain had a significant influence on the anxiety variable among the psychosocial factors. CONCLUSION: These findings suggest that musculoskeletal pain provides meaningful information about depression, anxiety, and sleep disorder in veterans with PTSD. Our data suggest that musculoskeletal pain may need to be addressed as part of the health management process of veterans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".