Analgesic benefits of regional anesthesia in the perioperative management of transition-related surgery: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Transition-related surgery is an effective treatment for gender dysphoria, but the perioperative analgesic management of transgender patients is nuanced and potentially complicated by higher rates of mood and substance use disorders. Regional anesthetic techniques are known to reduce pain severity and opioid requirements; however, little is known regarding the relative analgesic effectiveness of regional anesthesia for transgender patients undergoing transition-related surgery. METHODS: We performed a systematic review of the literature to evaluate original reports characterizing the analgesic effectiveness of regional anesthetic techniques for patients undergoing chest and/or genital transition-related surgery. Our primary outcomes were pain severity and opioid requirements on the first postoperative day. RESULTS: Of the 1863 records identified, 10 met criteria for inclusion and narrative synthesis. These included two randomized controlled trials, three cohort studies, and five case reports/series, comprising 293 patients. Four reports described 243 patients undergoing chest surgery, of whom 86% were transgender men undergoing mastectomy with pectoralis nerve blocks or local anesthetic instillation devices. The remaining six reports comprised 50 patients undergoing genital surgery, of whom 56% were transgender women undergoing vaginoplasty with erector spinae plane blocks or epidural anesthesia. Three studies directly compared regional techniques to parenteral analgesia alone. Two of these studies reported lower pain scores and opioid requirements on the first postoperative day with nerve blocks compared with none while the third study reported no difference between groups. Complications related to regional anesthetic techniques were rare among patients undergoing transition-related surgery. DISCUSSION: Despite the ever-growing demand for transition-related surgery, the relative analgesic effectiveness of regional anesthesia for transgender patients undergoing transition-related surgery is very understudied and insufficient to guide clinical practice. Our systematic review of the literature serves to underscore regional anesthesia for transition-related surgery as a priority area for future research.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".