Denervation as a Treatment for Arthritis of the Hands: A Systematic Review of the Current Literature
Bibliographic record
Abstract
Joint denervation has been proposed as a less invasive option for surgical management of hand arthritis that preserves joint anatomy while treating pain and decreasing postoperative recovery times. The purpose of this systematic review was to investigate the efficacy and safety of surgical joint denervation for osteoarthritis in the joints of the hand. EMBASE, MEDLINE, and PubMed databases were searched from January 2000 to March 2019. Studies of adult patients with rheumatoid arthritis or osteoarthritis of the hand who underwent joint denervation surgery were included. Two reviewers performed the screening process, data abstraction, and risk of bias assessment (Methodological Index for Non-Randomized Studies). This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was registered with PROSPERO (#125811). Ten studies were included, 9 case series and 1 cohort study, with a total of 192 patients. In all studies, joint denervation improved pain and hand function at follow-up (M = 36.8 months, range = 3-90 months). Pooled analysis of 3 studies on the first carpometacarpal joint showed a statistically significant ( P < .001) reduction in pain scores from baseline (M = 6.61 ± 2.03) to postoperatively (M = 1.69 ± 1.27). The combined complication rate was 18.8% (n = 36 of 192), with neuropathic pain or unintended sensory loss (8.8%, n = 17 of 192) being the most common. This review suggests that denervation may be an effective and low-morbidity procedure for treating arthritis of the hand. Prospective, comparative studies are required to further understand the outcomes of denervation compared with traditional surgical interventions.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| 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".