Pain Management for Ambulatory Arthroscopic Anterior Cruciate Ligament Reconstruction: Evidence-Based Recommendations From the Society for Ambulatory Anesthesia
Bibliographic record
Abstract
Ambulatory arthroscopic anterior cruciate ligament reconstruction is associated with moderate pain, even when nonopioid oral analgesics such as acetaminophen and nonsteroidal anti-inflammatory drugs are used. Regional analgesia can supplement nonopioid oral analgesics and reduce postoperative opioid requirements, but the choice of regional analgesia technique for anterior cruciate ligament reconstruction remains controversial. Femoral nerve block, adductor canal block, and local instillation analgesia have all been proposed and are supported by some evidence from randomized controlled trials. Consequently, regional analgesia practice in patients undergoing anterior cruciate ligament reconstruction remains mixed. Published systematic reviews were used to identify the regional analgesia modality that would provide a balance between analgesic efficacy and associated potential risks in the setting of nonopioid multimodal analgesic strategies. Based on the evidence available, local instillation analgesia provides the best balance of analgesic efficacy and associated risks (strong recommendation, moderate level of evidence) when used as a component of multimodal analgesic technique in the first 24 hours after outpatient arthroscopic anterior cruciate ligament reconstruction. In the absence of local instillation analgesia, clinicians might use adductor canal block or femoral nerve block (weak recommendation, weak level of evidence). These recommendations have been endorsed by the Society of Ambulatory Anesthesia and approved by its board of directors.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".