Biomedical and Psychosocial Factors Associated with Pain and Disability after Peripheral Nerve Injury
Bibliographic record
Abstract
The main objective of my dissertation was to evaluate the biomedical and psychosocial factors associated with pain and disability in patients following traumatic upper extremity nerve injuries. This was approached by conducting 3 studies. The first study surveyed peripheral nerve surgeons regarding the assessment of pain in patients with nerve injury. The results showed that only 52% of surgeons always evaluate pain in patients referred for motor/sensory dysfunction. Pain assessment frequently includes verbal response and assessment of psychosocial factors is infrequent. The second study was a retrospective review to assess disability, as measured by the Disabilities of the Arm, Shoulder and Hand (DASH), in patients with chronic nerve injury. Results showed substantial disability (mean DASH 52 + 22) and a significantly lower health status (p < 0.001) compared with well-established norms. In the regression model, the factors associated with the DASH (R2 = 44.5%) were pain, older age and nerve injured. The third study was a cross-sectional evaluation of the biomedical and psychosocial factors associated with pain and disability after upper extremity nerve injury in 158 patients. DASH scores were significantly higher in patients with workers’ compensation or litigation (p = 0.03), brachial plexus injuries (p < 0.001) and unemployed patients (p < 0.001). In the multivariable regression analysis, the final model explained 52.7% of the variance with these predictors; pain intensity (Beta = .230, p = 0.006), nerve injured (Beta = -.220, p = 0.000), time since injury (Beta = -.198, p = 0.002), pain catastrophizing (Beta = .192, p = 0.025), age (Beta = .187, p = 0.002), work status (Beta = .179, p = 0.008), cold sensitivity (Beta = .171, p = 0.015), depression score (Beta = .133, p = 0.066), workers’ compensation/litigation (Beta = .116, p = 0.049) and gender (Beta = -.104, p = 0.09). Future investigation regarding treatments of the factors that are associated with disability and chronic pain will assist to improve health related quality of life in patients with traumatic nerve injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".