Combat Posttraumatic Stress Disorder and Chronic Pain
Bibliographic record
Abstract
chronic post-traumatic stress disorder (PTSD) is commonly accompanied by depression and anxiety as comorbidity. The psychological states, such as depression and anxiety, can increase pain symptoms. A number of recent research results have shown that chronic post-traumatic stress disorder and chronic pain frequently co-occur and similar mechanisms have been identified that sustain both conditions. Method: the data were collected from medical records of 184 Croatian war veterans diagnosed with chronic PTSD and chronic pain as co-morbid condition. On the basis of medical records, interviews and different types of self-assessment questionnaires the inter-relationship between chronic pain and chronic PTSD was analysed. PTSD was assessed by CAPS (Clinical Administered Posttraumatic Scale) and M-PTSD (Mississippi Scale for combat PTSD), whereas pain was measured by Melzack-McGill Pain Questionnaire—short form (MPQ-SF) and Visual Analogue Scale (VAS). Results: the combat veterans with PTSD reported in descending order the following: pain in the head, back pain, widespread pain and limb pain. The patients with chronic PTSD had significantly higher total pain scores as well as affective and sensory pain components when compared to the patients without PTSD. Anxiety and depression were also highly correlated with pain. The relation between pain severity and depression was mediated by the severity of PTSD. Conclusion: our findings are directed towards the need for multidisciplinary approach in the treatment of patients with chronic PTSD and co-morbid chronic pain, which will optimize treatment and result in more cost-effective care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".