Quality of Life and Psychosocial Factors as Predictors of Pain Relief Following Nerve Surgery
Bibliographic record
Abstract
Background: Peripheral nerve injuries may result in pain, disability, and decreased quality of life (QoL). Pain is an incompletely understood experience and is associated with emotional and behavioral qualities. We hypothesized that pain following peripheral nerve surgery could be predicted by changes in emotions or QoL postoperatively. Methods: Using prospectively collected data, a retrospective study design was used to evaluate the relationships among pain, QoL, and psychosocial factors in patients who underwent peripheral nerve surgery. Patients completed questionnaires rating pain; impact of pain on QoL, sadness, depression, frustration, anger, and hopefulness before surgery; and each postoperative follow-up visit. Multilevel modeling was used to assess the concurrent and lagged relationships between pain and psychosocial factors. Results: Increased pain was concurrently associated with decreased hopefulness ( P = .001) and increased the impact on QoL, sadness, depression, and anger ( P < .001). In lagged analyses, the impact on QoL and anger prospectively predicted pain ( P < .001 and P = .02, respectively). Pain predicted subsequent scores of QoL, sadness, depression, anger, and hopefulness ( P < .01). Having an upper limb nerve injury and self-report of “no comment for childhood trauma” were predictors of postsurgical pain. Conclusion: Psychosocial measures and pain are reciprocally related among patients who underwent surgery for peripheral nerve injuries or compression. Our study provides evidence of the important relationships among psychosocial factors, pain, and outcome and identifies treatment targets following nerve surgery.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".