Local Infiltration Analgesia Versus Adductor Canal Block for Managing Pain After Anterior Cruciate Ligament Reconstruction: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Adductor canal block (ACB) and local infiltration analgesia (LIA) are frequently used to manage pain in patients after anterior cruciate ligament reconstruction (ACLR). Purpose: To compare the difference in pain scores and opioid consumption between ACB and LIA for ancillary pain management in patients after ACLR. Study Design: Systematic review; Level of evidence, 3. Methods: A literature search was conducted using PubMed, MEDLINE, and Embase databases according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies that compared pain scores at 2, 6, 12, or 24 hours after ACLR or provided information on 24-hour opioid consumption were included. Of 240 publications initially screened by abstract and title, 4 studies were included, and data related to participant characteristics, anesthetic technique, and pain-related outcomes were extracted. The standardized mean difference (MD) in pain scores and morphine milligram equivalents consumed in 24 hours was compared using a random-effects model. Results: In all studies, ropivacaine was the primary anesthetic used for LIA and ACB, with 1 study also employing bupivacaine as an alternative. The difference in pain scores between LIA and ACB was not significant at 2 hours (MD, 0.04 [95% CI, –0.22 to 0.29]; P = .79), 6 hours (MD, 0.16 [95% CI, –0.20 to 0.52]; P = .39), 12 hours (MD, 0.54 [95% CI, –0.49 to 1.56]; P = .31), or 24 hours (MD, 0.12 [95% CI, –0.10 to 0.34]; P = .28). The difference in morphine milligram equivalents was also not statistically significant (MD, –0.07 [95% CI, –0.25 to 0.11]; P = .68). Conclusion: From this review, the authors suggest considering LIA over ACB because of its potential to offer comparable pain relief and opioid consumption while being less time intensive. However, the study results should be interpreted with caution, given the limited number of studies included.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".