The Effect of Cryotherapy Application on Postoperative Pain
Bibliographic record
Abstract
OBJECTIVE: To systematically review and meta-analyze whether the application of cryotherapy on closed incisions reduces postoperative pain and opioid consumption. BACKGROUND: Reduction of acute pain and opioid use is important in the postoperative phase of patient care. ''Cryotherapy'' refers to the use of low temperatures for therapeutic purposes. METHODS: MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials and Online registries of clinical trial were search until October 2019. RCT that examined postoperative application of cryotherapy over surgical incisions in adults compared to no cryotherapy were eligible. Selection, extraction, and risk of bias appraisal were completed in duplicate. Data were synthesized using random effects meta-analyses. The outcomes of interest were postoperative pain, opioid use, hospital length of stay (LOS) and surgical site infection (SSI). RESULTS: Fifty-one RCTs (N = 3425 patients) were included. With moderate certainty evidence, patients treated with cryotherapy experienced a reduction in pain on postoperative day 1 (standardized mean differences -0.50, 95% CI -0.71 to -0.29, l 2= 74%) and day 2 (standardized mean differences -0.63, 95% CI -0.91 to -0.35, I 2 = 83%) relative to without cryotherapy application. With moderate certainty of evidence, cryotherapy reduces opioid consumption in morphine milliequivalents and morphine milliequivalents/kg, (mean differences -7.43, 95% CI -12.42, -2.44, I 2 = 96%) and (mean differences -0.89, 95% CI -1.45, -0.33, I 2 = 99%), respectively. With low certainty evidence, cryotherapy does not affect hospital LOS or rate of SSI. CONCLUSION: Cryotherapy is a pragmatic, noncostly intervention that reduces postoperative pain and opioid consumption with no effect on SSI rate or hospital LOS.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".