Factors associated with rebound pain after peripheral nerve block for ambulatory surgery
Bibliographic record
Abstract
BACKGROUND: Rebound pain is a common, yet under-recognised acute increase in pain severity after a peripheral nerve block (PNB) has receded, typically manifesting within 24 h after the block was performed. This retrospective cohort study investigated the incidence and factors associated with rebound pain in patients who received a PNB for ambulatory surgery. METHODS: Ambulatory surgery patients who received a preoperative PNB between March 2017 and February 2019 were included. Rebound pain was defined as the transition from well-controlled pain (numerical rating scale [NRS] ≤3) while the block is working to severe pain (NRS ≥7) within 24 h of block performance. Patient, surgical, and anaesthetic factors were analysed for association with rebound pain by univariate, multivariable, and machine learning methods. RESULTS: Four hundred and eighty-two (49.6%) of 972 included patients experienced rebound pain as per the definition. Multivariable analysis showed that the factors independently associated with rebound pain were younger age (odds ratio [OR] 0.98; 95% confidence interval [CI] 0.97-0.99), female gender (OR 1.52 [1.15-2.02]), surgery involving bone (OR 1.82 [1.38-2.40]), and absence of perioperative i.v. dexamethasone (OR 1.78 [1.12-2.83]). Despite a high incidence of rebound pain, there were high rates of patient satisfaction (83.2%) and return to daily activities (96.5%). CONCLUSIONS: Rebound pain occurred in half of the patients and showed independent associations with age, female gender, bone surgery, and absence of intraoperative use of i.v. dexamethasone. Until further research is available, clinicians should continue to use preventative strategies, especially for patients at higher risk of experiencing rebound pain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".