Adjuncts to local anesthetic wound infiltration for postoperative analgesia: a systematic review
Bibliographic record
Abstract
Local anesthetics (LAs) are commonly infiltrated into surgical wounds for postsurgical analgesia. While many adjuncts to LA agents have been studied, it is unclear which adjuncts are most effective for co-infiltration to improve and prolong analgesia. We performed a systematic review on adjuncts (excluding epinephrine) to local infiltrative anesthesia to determine their analgesic efficacy and opioid-sparing properties. Multiple databases were searched up to December 2019 for randomized controlled trials (RCTs) and two reviewers independently performed title/abstract screening and full-text review. Inclusion criteria were (1) adult surgical patients and (2) adjunct and LA agents infiltration into the surgical wound or subcutaneous tissue for postoperative analgesia. To focus on wound infiltration, studies on intra-articular, peri-tonsillar, or fascial plane infiltration were excluded. The primary outcome was reduction in postoperative opioid requirement. Secondary outcomes were time-to-first analgesic use, postoperative pain score, and any reported adverse effects. We screened 6670 citations, reviewed 126 full-text articles, and included 89 RCTs. Adjuncts included opioids, non-steroidal anti-inflammatory drugs, steroids, alpha-2 agonists, ketamine, magnesium, neosaxitoxin, and methylene blue. Alpha-2 agonists have the most evidence to support their use as adjuncts to LA infiltration. Fentanyl, ketorolac, dexamethasone, magnesium and several other agents show potential as adjuncts but require more evidence. Most studies support the safety of these agents. Our findings suggest benefits of several adjuncts to local infiltrative anesthesia for postoperative analgesia. Further well-powered RCTs are needed to compare various infiltration regimens and agents. PROTOCOL REGISTRATION: PROSPERO (CRD42018103851) (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=103851).
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".