Pain Severity Ratings Among Patients with Comorbid Chronic Pain and PTSD: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Posttraumatic stress disorder (PTSD) is a psychiatric disorder that may occur after experiencing or witnessing a traumatic event. PTSD is characterized by physiological symptoms such as sleep disturbances and hyperarousal. One understudied symptom in PTSD patients is chronic pain (CP). Acute pain can lead to CP when it persists beyond adaptation. The interconnection between stress and pain has been well-established in fields of neuroscience and psychology, though we still do not fully understand the nature of this clinical relationship. Methods: In the current retrospective cohort study, we use a sample of patients with PTSD and CP through a database of numerous healthcare organizations called TriNetX. We compare the reported pain severity rating between three groups: those with PTSD and no CP, those with CP and no PTSD, and those with comorbid PTSD and CP. The summary data was compared using a one-way analysis of variance with the Welch statistic. Results: The average reported pain severity was significantly different between all three groups (F(2, 21288)=279.80, p < .001). The patients with comorbid PTSD and CP reported the highest average pain severity, followed by patients with only CP and then patients with only PTSD. Conclusion: Our results demonstrate a need to further investigate the complex relationship between PTSD and CP. The higher average pain severity in patients with both disorders suggest that integrated pain management and mental health interventions must be prioritized in this population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".