Efficacy of different routes of dexamethasone administration for preventing rebound pain following peripheral nerve blocks in adult surgical patients: a systematic review and network meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Rebound pain, characterised by intense pain or discomfort as the effects of a peripheral nerve block diminish, remains a clinical problem. Peri-operative dexamethasone administration may reduce the incidence of rebound pain. This systematic and network meta-analysis aimed to determine the optimal route of dexamethasone administration for the prevention of rebound pain. METHODS: We searched databases for randomised controlled trials according to pre-determined criteria. We compared intravenous and perineural dexamethasone as an adjunct to peripheral nerve blocks, with the control group as a common comparator. The primary outcome was the incidence of rebound pain. The likelihood of an intervention ranking highest was calculated using the surface area under the cumulative ranking curve. RESULTS: In total, 14 studies with 1058 patients were included. When compared with the comparator group, we found that intravenous dexamethasone ranked the highest, with an anticipated effect of 298 fewer cases of rebound pain per 1000 people (odds ratio (OR) (95% credible interval (CrI) 0.12 (0.03-0.44)); moderate certainty evidence). This was followed by perineural dexamethasone with an anticipated effect of 190 fewer cases per 1000 people (OR (95%CrI) 0.34 (0.07-1.32); low certainty evidence). There was no evidence of an effect between the route of administration and time to onset of rebound pain. DISCUSSION: Intravenous dexamethasone was associated with a high probability of decreasing the incidence of rebound pain following peripheral nerve block. This is based on moderate certainty of evidence. Future studies on identifying the optimal dose are now warranted.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".