Efficacy of Liposomal Bupivacaine in Arthroscopic Rotator Cuff Repair: a Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Objective. To conduct the first-ever meta-analysis of all randomized controlled trials (RCTs) that scrutinized the analgesic efficacy of liposomal bupivacaine (LB, intervention) versus nonliposomal local anesthetic agents (NLAAs, control) during arthroscopic rotator cuff repair (ARCR). Methods. Five databases were screened from inception until 09-April-2022. Subgroup analyses according to the postoperative day (POD, POD 0-7) were conducted. The data were summarized as weighted mean difference (WMD) with 95% confidence interval (CI) under the random-effects model. Results. Seven RCTs comprising 442 patients were included. Three, three, and one RCT(s) were judged to have an overall “low”, “high”, and “unclear” risk of bias, respectively. Regarding overall VAS pain score, there was no significant difference between both groups (WMD = -0.56, 95% CI [-1.16, 0.04], p = 0.07). Subgroup analyses showed significantly reduced postoperative VAS pain scores in favor of the LB group on POD 1/2; however, these reductions were not clinically meaningful. Also, LB correlated with a significant reduction in overall postoperative opioid consumption (WMD = -7.72 MME, 95% CI [-11.48, -3.97], p < 0.001). Subgroup analyses showed a significantly reduced postoperative opioid consumption in favor of the LB group on POD 1/2/3; however, these reductions were not clinically meaningful. Conclusions. Among patients undergoing ARCR, this systematic review and meta-analysis of RCTs showed that LB did not correlate with clinically meaningful reductions in postoperative VAS pain scores and overall opioid consumption. Future large-sized and well-designed RCTs are needed to consolidate the presented evidence.
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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 | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.035 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".