Cryoneurolysis for non-cancer knee pain: A scoping review
Bibliographic record
Abstract
Background and objective: Cryoneurolysis involves percutaneous insertion of a cryoprobe induced to extremely cold temperatures to disrupt peripheral nerve conduction. The primary objective of this scoping review is to summarize and critically appraise the current evidence for the benefits and safety of cryoneurolysis for non-cancer knee pain. The secondary objective is to describe the variations in cryoneurolysis techniques used. Methods: MEDLINE, EMBASE, PubMed, Cochrane Library, and Web of Science were searched from their inception to February 2023 for any primary literature investigating the use of cryoneurolysis for non-cancer-related knee pain. Data was extracted for study characteristics, intervention characteristics, and clinical outcomes. Results: Fourteen studies were identified, including three randomized controlled trials, four retrospective cohort studies, and seven case studies/series. Two studies included knee osteoarthritis patients, three studies included non-specific chronic knee pain patients; and nine studies included pre- or post-total knee arthroplasty patients. Ten studies targeted the infrapatellar branch of the saphenous nerve while the remaining four studies did not report the nerve targeted. Studies consistently demonstrated improvements in pain, function, quality of life, and opioid consumption. Most adverse events were mild and self-limiting. Considerable variations in technique parameters were observed. Conclusions: Cryoneurolysis is a promising intervention to improve outcomes in non-cancer knee pain populations, particularly in mild-to-moderate knee osteoarthritis and pre-total knee arthroplasty populations. However, cryoneurolysis for knee pain remains largely investigational as more high-quality randomized controlled trials are required to further elucidate efficacy as well as optimal nerve selection and technique.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".