Denervation as a Treatment for Arthritis in the Hands: A Systematic Review of Current Literature
Bibliographic record
Abstract
PURPOSE: Osteoarthritis is a common degenerative condition that can cause pain and swelling in the joints of the hand. Current surgical treatments include osteotomy, arthroplasty, and arthrodesis. However, each of these interventions is invasive and carry inherent limitations such as high-complication rates, altered joint anatomy, and prolonged recovery times. Joint denervation, which involves selective neurectomy of the articular nerve branches to an osteoarthritic joint, has been proposed as a less invasive treatment option. The purpose of this systematic review was to evaluate and report the efficacy and safety of surgical joint denervation for osteoarthritis in joints of the hand. METHODS: Embase, MEDLINE, and PubMed databases were searched from January 2000 to March 2019. Studies of adult patients with rheumatoid or osteoarthritis of the hand who underwent joint denervation surgery were included. Two independent reviewers performed the screening process, data abstraction, and quality assessment (MINORS). PRISMA guidelines were followed, and this review was registered with PROSPERO (#125811). RESULTS: Ten studies were included, 9 case series and 1 cohort study, with a total of 211 hand joint denervation in 192 patients. In all studies, joint denervation improved pain and hand function at follow-up (mean = 36.8; range = 3–90 months). Pooled analysis of 3 studies on the first carpometacarpal joint showed a statistically significant (P < 0.001) reduction in pain scores from baseline (mean = 6.61 ± 2.03) to postoperative (mean = 1.69 ± 1.27). The combined complication rate was 18.8% (n = 36/192), and the most commonly reported complication was neuropathic pain or unintended sensory loss (8.8%; n = 17/192). CONCLUSIONS: The findings of this review suggest that denervation may be an effective and low morbidity procedure for the treatment of osteoarthritis in the joints of the hand. Denervation may serve as a useful tool in the hand surgeon’s armamentarium, particularly for patients failing nonoperative management and looking to pursue less invasive surgical management. Further prospective comparative studies are required to develop a more comprehensive understanding of the outcomes of denervation, especially in comparison to more conventional procedures of osteotomy, arthroplasty, and arthrodesis.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".