Epidemiology and Treatment of Peripheral Neuropathy in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: The epidemiology and treatment of peripheral neuropathy in systemic sclerosis (SSc) is poorly understood. The objectives of this study were to evaluate the incidence, prevalence, risk factors, and treatments of peripheral neuropathy in SSc. METHODS: A systematic review of MEDLINE, Embase, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases for literature reporting peripheral neuropathy in SSc was performed. Studies evaluating incidence, prevalence, risk factors, and treatments were synthesized. A metaanalysis using a random effects model was used to evaluate the prevalence of peripheral neuropathy. RESULTS: This systematic review identified 113 studies that reported 949 of 2143 subjects with at least 1 type of peripheral neuropathy. The mean age was 48.5 years. The mean time between SSc onset and detection of peripheral neuropathy was 8.85 years. The pooled prevalence of neuropathy was 27.37% (95% CI 22.35-32.70). Risk factors for peripheral neuropathy in SSc included advanced diffuse disease, anticentromere antibodies, calcinosis cutis, ischemia of the vasa nervorum, iron deficiency anemia, metoclopramide, pembrolizumab, silicosis, and uremia. There were 73 subjects with successful treatments (n = 36 restoring sensation, n = 37 restoring motor or sensorimotor function). Treatments included decompression surgery, prednisone, cyclophosphamide, carbamazepine, transcutaneous electrical nerve stimulation, tricyclic antidepressants, and intravenous Ig. CONCLUSION: All-cause peripheral neuropathy is not uncommon in SSc. Compression neuropathies can be treated with decompression surgery. Observational data reporting immunosuppressives and anticonvulsants to treat peripheral neuropathy in SSc are limited and conflicting. Randomized controlled trials are needed to evaluate the efficacy of these 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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".