Upper-Extremity Nerve Decompression Under Local Anesthesia: A Systematic Review of Methods for Reduction of Postoperative Pain and Opioid Consumption
Bibliographic record
Abstract
Background: Opioid abuse is a major health concern in North America. Data have shown an alarming increase in opioid-related deaths and complications, which has shed light on current prescription practices across many specialties, including hand surgery. To that end, we sought to conduct a focused literature review to determine the available modalities to decrease postoperative pain and opioid consumption following upper-extremity nerve decompression procedures, taking advantage of the homogeneity and inherent pain pathways of this specific patient cohort. Methods: A systematic review of the literature was conducted. Primary studies evaluating perioperative and intraoperative modalities for postoperative pain reduction and analgesic consumption following upper-extremity nerve decompression procedures under local anesthesia were included. Studies implementing modalities requiring non–hand surgeon expertise (ie, intravenous sedation), as well as studies that include non–nerve decompression procedures, were excluded. Results: A total of 1478 studies were identified, and 9 studies were included after full-text review. Six studies evaluated intraoperative and 3 studies evaluated preoperative and postoperative modalities. Successful interventions included buffered anesthetic, the use of hyaluronidase, and varying techniques and mixtures for anesthetic administration. No successful preoperative or postoperative modalities were identified. Conclusion: Despite data reporting on the dangers associated with current opioid prescription practices, evidence-based modalities to decrease postoperative pain and opioid consumption are limited in general. Several intraoperative modalities do exist, and nonopioid oral analgesics, standardized opioid protocols, and reduced postoperative prescriptions can be used. Large randomized controlled trials evaluating perioperative modalities for pain reduction are needed to further address this issue.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".