Prevalence of chronic pain seven years following limb threatening lower extremity trauma ☆
Bibliographic record
Abstract
Although the etiology of chronic pain following trauma is not well understood, numerous retrospective studies have shown that a significant proportion of chronic pain patients have a history of traumatic injury. The present analysis examines the prevalence and early predictors of chronic pain in a cohort of prospectively followed severe lower extremity trauma patients. Chronic pain was measured using the Graded Chronic Pain Scale, which measures both pain severity and pain interference with activities. Severe lower extremity trauma patients report significantly higher levels of chronic pain than the general population (p<0.001). Their levels are comparable to primary care migraine headache and back pain populations. A number of early predictors of chronic pain were identified, including: having less than a high school education (p<0.01), having less than a college education (p<0.001), low self-efficacy for return to usual major activities (p<0.01), and high levels of average alcohol consumption at baseline (p<0.05). In addition, high reported pain intensity, high levels of sleep and rest dysfunction, and elevated levels of depression and anxiety at 3 months post-discharge were also strong predictors of chronic pain at seven years (p<0.001 for all three predictors). After adjusting for early pain intensity, patients treated with narcotic medication during the first 3 months post-discharge had lower levels of chronic pain at 84 months. It is possible that for patients within these high risk categories, early referral to pain management and/or psychologic intervention may reduce the likelihood or severity of chronic pain.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".