Lower Extremity Nerve Decompression for Diabetic Peripheral Neuropathy: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Diabetic peripheral neuropathy (DPN) is a leading cause of morbidity. This systematic review and meta-analysis evaluate the efficacy of lower extremity nerve decompression in reducing DPN symptoms and complications. Methods: A database search was performed using Medline, Embase, Google Scholar, and Cochrane Central Register of Controlled Trials. Articles addressing surgical decompression of lower limb peripheral nerves in patients with diabetes were screened for inclusion. Two independent reviewers undertook the assessment. Methodological quality measures were the Cochrane risk of bias and Newcastle-Ottawa scale. Results: The pooled sample size from 21 studies was 2169 patients. Meta-analysis of 16 observational studies showed significant improvement in the visual analog scale (VAS) (P < 0.00001) and two-point discrimination (P = 0.003), with strong reliability. Decompression of the tarsal tunnel region had the highest improvement in VAS [MD, 6.50 (95% CI, 3.56–9.44)]. A significant low-risk ratio (RR) of ulcer development and lower limb amputation was detected (P < 0.00001). Lowest RR of ulcer development was detected with tarsal tunnel release [RR, 0.04 (95% CI, 0.00–0.48)]. Improvements in VAS, two-point discrimination, and nerve conduction velocity were nonsignificant in the meta-analysis of five randomized controlled trials (RCTs). The RCT analysis was limited to only two studies for each outcome. Conclusions: Meta-analysis of observational studies highlights the efficacy of lower extremity nerve decompression in reducing DPN symptoms, ulcerations, and amputations. Releasing the tibial nerve in the tarsal tunnel region was the most effective observed procedure. Nevertheless, high-quality RCTs are required to support the utility of this intervention in DPN.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".