Analgesic efficacy of local infiltration analgesia vs. femoral nerve block after anterior cruciate ligament reconstruction: a systematic review and meta‐analysis
Bibliographic record
Abstract
Many published reports consider blockade of the femoral nerve distribution the best available analgesic treatment after anterior cruciate ligament reconstruction. However, some argue that an alternative approach of infiltrating local anaesthetic into the surgical site has similar efficacy. The objectives of this meta-analysis were to compare the analgesic and functional outcomes of both treatments following anterior ligament reconstruction. The primary outcomes were pain scores at rest (analogue scale, 0-10) in the early (0-2 postoperative hours), intermediate (3-12 hours) and late postoperative periods (13-24 hours). Secondary outcomes included range of motion, quadriceps muscle strength and complication rates (neurological problems, cardiovascular events, falls and knee infections). Eleven trials, including 628 patients, were identified. Pain scores in the early, intermediate and late postoperative periods were significantly lower in patients who received a femoral nerve block, with mean differences (95%CI) of 1.6 (0.2-2.9), p = 0.02; 1.2 (0.4-1.5), p = 0.002; and 0.7 (0.1-1.4), p = 0.03 respectively. The quality of evidence for our primary outcomes was moderate to high. Regarding functional outcomes, only one trial reported a similar range of motion between groups at 48 postoperative hours. No trial sought to record complications. In conclusion, femoral nerve block provides superior postoperative analgesia after anterior cruciate ligament reconstruction to local infiltration analgesia. The impact of improved analgesia on function remains unclear due to the lack of reporting of functional outcomes in the existing literature.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".