Cryoanalgesia for postsurgical pain relief in adults: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Despite advances in pain management, postoperative pain continues to be an important problem with significant burden. Many current therapies have dose-limiting adverse effects and are limited by their short duration of action. This review examines the evidence for the efficacy and safety of cryoanalgesia in postoperative pain. MATERIALS AND METHODS: This review was registered in PROSPERO and prepared in accordance with PRISMA. MEDLINE, EMBASE, and Cochrane databases were searched until July 2020. We included randomized controlled trials (RCTs) of adults evaluating perioperatively administered cryoanalgesia for postoperative pain relief. RESULTS: Twenty-four RCTS were included. Twenty studies examined cryoanalgesia for thoracotomy, two for herniorrhaphy, one for nephrectomy and one for tonsillectomy. Meta-analysis was performed for thoracic studies. We found that cryoanalgesia with opioids was more efficacious than opioid analgesia alone for acute pain (mean difference [MD] 2.32 units, 95 % confidence interval [CI] -3.35 to -1.30) and persistent pain (MD 0.81 units, 95 % CI -1.10 to -0.53) after thoracotomy. Cryoanalgesia with opioids also resulted in less postoperative nausea compared to opioid analgesia alone (relative risk [RR] 0.23, 95 % CI 0.06 to 0.95), but there was no difference in atelectasis (RR 0.38, 95 % CI 0.07 to 2.17). CONCLUSION: Heterogeneity in comparators and outcomes were important limitations. In general, reporting of adverse events was incomplete and inconsistent. Many studies were over two decades old, and most were limited in how they described their methodology. Considering the potential, larger RCTs should be performed to better understand the role of cryoanalgesia in postoperative pain management.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".