Preventing persistent postsurgical pain: A systematic review and component network meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Evidence for perioperative methods to prevent persistent postsurgical pain (PPP) is uncertain, in part because few treatments have been directly compared. Here we have used component network meta-analysis (cNMA) to incorporate both direct and indirect evidence in the evaluation of the efficacy and tolerability of pharmacological and neural block treatments. DATABASES AND DATA TREATMENT: We searched the Cochrane Central Registry of Controlled Trials, Embase, MEDLINE, ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry up to January 2021 for randomized, double-masked, controlled trials that reported the prevalence of PPP. We assessed trial quality with the Cochrane risk of bias tool (RoB 2.0). We analysed the results with frequentist cNMA models. The primary outcome was the relative risk (RR) of PPP. We assessed efficacy in relation to a clinically important effect size of RR = 0.9, which is a 10% improvement with treatment. RESULTS: The analysis included 107 trials (13,553 participants) of 13 treatments. The effects of complex interventions were the multiplicative effects of their components. Compared with placebo, serotonin-norepinephrine reuptake inhibitors (SNRIs), neural block alone, or in combination with NMDA receptor blockers or gabapentanoids were effective. Treatments with benefit in the immediate post-operative period predicted a reduced risk of PPP. CONCLUSIONS: Several treatments and treatment combinations effectively reduce PPP prevalence. Pain outcomes in the immediate postoperative period are an important mediator of PPP. Multimodal interventions can be analysed using cNMA. SIGNIFICANCE: Systematic reviews of PPP prevention usually focus on the efficacy of specific treatments in comparison with control interventions. In this study we used component network meta-analysis to compare interventions to each other, including both pharmacological and neural block techniques, and multimodal interventions. Interventions that are not effective alone may improve the efficacy of multimodal interventions that include neural block techniques. Immediate postoperative benefit was an important mediator for reduction of PPP. STUDY REGISTRATION: PROSPERO: CRD42018085570 https://www.crd.york.ac.uk/prospero/.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".