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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".