Efficacy of cryoneurolysis in the management of chronic non-cancer pain: A systematic review and meta-analysis
Bibliographic record
Abstract
Background and Aims: Cryoneurolysis, a neuroablative technique, is used in the event of failure of conservative treatment in chronic pain conditions. To date, no systematic review has been published to demonstrate its effectiveness in managing chronic non-cancer pain. Therefore, this review was done to ascertain the efficacy of cryoneurolysis and describe its role in chronic non-cancer pain management. Methods: We searched PubMed, Cochrane, Embase, Scopus, and Google Scholar databases for articles published between January 2011 and September 2021. Two independent reviewers extracted the data from the included studies. Assessment of risk of bias of included randomised controlled trials (RCTs) was done using RevMan 5.4.1 software and Newcastle-Ottawa scale was used for non-randomised studies. Results: Ten studies enroling a total of 425 patients were included in the qualitative analysis. Eight studies were assessed quantitatively. RCTs were found only for cervicogenic headache and knee osteoarthritis management. The rest of the included studies were prospective non-controlled and retrospective studies. A significant pain reduction was seen at seven-day [Standardised Mean Difference (SMD) 1.77 (1.07, 2.46)], P < 0.00001, I 2 = 79%), one-month (SMD 3.26 [2.60, 3.92], P < 0.00001, I 2 = 45%), three-month (SMD 2.58 [1.46, 3.70], P < 0.00001, I 2 = 93%), six-month (SMD 2.38 [0.97, 3.79], P = 0.001, I 2 = 86%) follow-ups. Improved disability and no serious complications were noted. Conclusion: Cryoneurolysis appeared to be effective in pain alleviation in refractory painful conditions for up to six months. It is safe and well-tolerated with an excellent safety profile but the quality of evidence is limited by substantial heterogeneity between trials. Therefore, more comparative clinical trials on a larger sample size are needed to provide more concrete evidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".