Predicting pain outcomes after traumatic musculoskeletal injury
Bibliographic record
Abstract
Traumatic musculoskeletal injury results in a high incidence of chronic pain; however, there is little evidence about the nature, quality, and severity of the pain. This study uses a prospective, observational, longitudinal design to (1) examine neuropathic pain symptoms, pain severity, pain interference, and pain management at hospital admission and 4 months after traumatic musculoskeletal injury (n = 205), and (2) to identify predictors of group membership for patients with differing moderate-to-severe putative neuropathic pain trajectories. Data were collected on mechanism of injury, injury severity, pain (intensity, interference, neuropathic quality), anxiety (anxiety sensitivity, general anxiety, pain catastrophizing, pain anxiety), depression, and posttraumatic stress while patients were in-hospital and 4 months after injury. A third of patients had chronic moderate-to-severe neuropathic pain 4 months after injury. Specifically, 11% of patients developed moderate-to-severe pain by 4 months and 21% had symptoms immediately after injury that persisted over time. Significant predictors of the development and maintenance of moderate-to-severe neuropathic pain included high levels of general anxiety while in-hospital immediately after injury (P < 0.001) and symptoms of posttraumatic stress 4 months after injury (P < 0.001). Few patients had adequate pharmacological, physical, or psychological pain management in-hospital and at 4 months. Future research is needed among trauma patients to better understand the development of chronic pain and to determine the best treatment approaches.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".