Perioperative lidocaine infusions for the prevention of chronic postsurgical pain: a systematic review and meta-analysis of efficacy and safety
Bibliographic record
Abstract
Chronic postsurgical pain (CPSP) occurs in 12% of surgical populations and is a high priority for perioperative research. Systemic lidocaine may modulate several of the pathophysiological processes linked to CPSP. This systematic review aims to identify and synthesize the evidence linking lidocaine infusions and CPSP. The authors conducted a systematic literature search of the major medical databases from inception until October 2017. Trials that randomized adults without baseline pain to perioperative lidocaine infusion or placebo were included if they reported on CPSP. The primary outcome was the presence of procedure-related pain at 3 months or longer after surgery. The secondary outcomes of pain intensity, adverse safety events, and local anesthetic toxicity were also assessed. Six trials from 4 countries (n = 420) were identified. Chronic postsurgical pain incidence was consistent with existing epidemiological data. Perioperative lidocaine infusions significantly reduced the primary outcome (odds ratio, 0.29; 95% confidence interval, 0.18-0.48), although the difference in intensity of CPSP assessed by the short-form McGill Pain Questionnaire (4 trials) was not statistically significant (weighted mean difference, -1.55; 95% confidence interval, -3.16 to 0.06). Publication and other bias were highly apparent, as were limitations in trial design. Each study included a statement reporting no adverse events attributable to lidocaine, but systematic safety surveillance strategies were absent. Current limited clinical trial data and biological plausibility support lidocaine infusions use to reduce the development of CPSP without full assurances as to its safety. This hypothesis should be addressed in future definitive clinical trials with comprehensive safety assessment and reporting.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".