Addition of Adductor Canal Block to Local Infiltration Analgesia Does Not Reduce Postoperative Opioid Use Following Anterior Cruciate Ligament Surgery
Bibliographic record
Abstract
PURPOSE: This propensity-matched cohort study aimed to determine if adding adductor canal block (ACB) to local infiltration analgesia (LIA) reduces immediate postoperative opioid use in anterior cruciate ligament (ACL) reconstruction and assess variations based on graft type. METHODS: This retrospective study analyzed ACL reconstructions performed from 2019 to 2021. Patients were included if they received either LIA alone or a combination of LIA and ACB. Patients were propensity-matched based on demographic and surgical factors, and perioperative opioid consumption was assessed. Subgroup analysis was conducted based on autograft type (hamstring, quadriceps tendon, and bone-patellar tendon-bone). RESULTS: No significant differences were observed in intraoperative, postanesthesia care unit, or total perioperative opioid consumption between the ACB + LIA group (27.76 ± 14.01 mg) and the LIA-only group (28.58 ± 12.56 mg). This finding was consistent across all autograft types. However, in the hamstring autograft subgroup, the addition of ACB led to a statistically significant reduction in postanesthesia care unit opioid consumption (30.99 vs 26.45 mg, P = .039), although this difference was not deemed clinically significant. Additionally, the ACB + LIA group experienced a significantly longer mean time to discharge (495 ± 113 minutes) compared to the LIA-only group (463 ± 116 minutes; P = .017). CONCLUSIONS: Our findings suggest that adding ACB to LIA does not provide additional opioid-sparing benefits in ACL reconstruction, except in patients with hamstring grafts, where the difference observed may not be of clinical significance. The increased discharge time with ACB highlights the need to balance benefits with operational efficiency. LEVEL OF EVIDENCE: Level III, retrospective matched comparative case series.
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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 | 0.004 |
| 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 | 0.000 |
| 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".