The benefits and risks of non‐steroidal anti‐inflammatory drugs for postoperative analgesia in sinonasal surgery: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) have emerged as an alternative to opioids for optimal postoperative pain management. However, the adoption of NSAIDs in sinonasal surgery has been impeded by a theoretical concern for postoperative bleeding. Our objective is to systematically review the efficacy and safety of NSAIDs for patients undergoing sinonasal surgery. METHODS: MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, CINAHL, and the WHO International Clinical Trials Registry Platform were searched from inception to January 27, 2022. Randomized controlled trials (RCTs) and comparative observational studies in any language were considered. Screening, data extraction, and risk of bias assessment were performed in duplicate. Our outcomes were postoperative pain scores, requirement for rescue analgesia, and postoperative adverse events (epistaxis, nausea/vomiting). RESULTS: Out of 4661 records, 15 RCTs (enrolling 1210 patients) and two observational studies were included. Following endoscopic sinus surgery, there was no difference in pain scores between NSAIDs and non-NSAIDs groups (standardized mean differences [SMD] 0.44 units better, 95% CI -0.18 to 1.05). Following septorhinoplasty, NSAIDs decreased pain scores compared to non-NSAID regimens (SMD 1.14 units better, 95% CI 0.61 to 1.67 units better). Overall, NSAIDs reduced the need for rescue medication with a relative risk (RR) of 0.45 (95% CI 0.24 to 0.84). In addition, NSAIDs decreased the risk of nausea with an RR of 0.62 (95% CI 0.42 to 0.91) and did not increase the risk of epistaxis (RR 0.72, 95% CI 0.23-2.22). CONCLUSION: Among patients undergoing sinonasal surgery, NSAIDs are beneficial in postoperative pain management and avoidance of postoperative nausea without increasing the risk of postoperative epistaxis.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".