Longitudinal course of posttraumatic stress disorder and chronic pain conditions: A population-based study of Canadian military personnel over 16 years
Bibliographic record
Abstract
BACKGROUND: Posttraumatic stress disorder (PTSD) and chronic pain are highly prevalent, comorbid, and debilitating conditions in the military. The present study was designed to examine the prevalence of chronic pain conditions (i.e., migraines, back problems, arthritis) across PTSD courses (i.e., no PTSD, remitted, new onset, persistent/recurrent) and examine the association between PTSD course and the presence and onset of chronic pain conditions in a population-representative sample of Canadian military members. METHODS: Cross-tabulations and logistic regressions were conducted on data (n = 2941) from the 2002 Canadian Community Health Survey Mental Health and Well-being Canadian Forces Supplement and the 2018 Canadian Armed Forces Members and Veterans Mental Health Follow-Up Survey. RESULTS: The prevalence of chronic pain conditions across PTSD courses ranged from 8 % to 61 %, with no PTSD consistently having the lowest prevalence. After adjusting for covariates, respondents with new onset PTSD had elevated odds of back problems (AOR=1.43, 95 % CI [1.10-1.90], p < .05), arthritis (AOR=1.46, 95 % CI [1.06-2.00], p < .05), and a new onset chronic pain condition more broadly (AOR=1.66, 95 % CI [1.15-2.39], p < .01), compared to those with no PTSD. Those with remitted PTSD had greater odds of migraines (AOR=2.43, 95 % CI [1.29-4.58], p < .01), while those with persistent PTSD had lower odds of back problems (AOR=0.45, 95 % CI [0.23-0.88], p < .05), compared to those with no PTSD. CONCLUSION: Findings indicate that the prevalence and type of chronic pain that co-occurs with PSTD in Canadian military members varies as a function of the course of PTSD. This underscores the importance of evaluating pain in those with PTSD and suggests that the course of PTSD is a relevant consideration in case conceptualization and treatment planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".