An examination of the relationship between persistent traumatic brain injuries and chronic pain conditions in active-duty and Veteran soldiers: results from the Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey
Bibliographic record
Abstract
Traumatic brain injuries (TBI) are highly prevalent among military personnel. TBIs have been shown to be associated with lasting negative physical health, mental health, and psychosocial consequences. Chronic pain is one common comorbidity for those experiencing persistent effects of a TBI. This study examined the relationship between persistent TBI and chronic pain conditions (i.e., arthritis, back problems, gastrointestinal conditions, and migraine headaches) in a Canadian military sample, including potential preinjury characteristics (i.e., sociodemographic and military demographics), mental health disorders (e.g., depression, PTSD) and other biopsychosocial factors (i.e., social support, sleep difficulty), as well as potential moderators (i.e., sex and serving status) that impact this relationship. This study utilized data from the Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey (CAFMVHS) collected in 2018 (n = 2,941). Logistic regression analyses demonstrated arthritis, back problems, migraine headaches, and gastrointestinal conditions to be significantly associated with persistent TBI. Preinjury characteristics (i.e., sociodemographic, and military demographics) associated with comorbid persistent TBI and any chronic pain condition(s) included military rank and serving status. The comorbidity of any mental health disorders was not significantly more associated with persistent TBI and any chronic pain condition(s). Biopsychosocial factors associated with comorbid persistent TBI, and any chronic pain condition(s) included problem-solving coping and sleep difficulties. Findings identify the prevalence of chronic pain conditions among CAF members with persistent TBI and provide further insight potential risk factors that may influence this relationship including military characteristics, mental health disorders, and other biopsychosocial factors.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".