Peripheral neuropathies in rheumatic diseases: More diverse and frequent than expected. A cross‐sectional study
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Peripheral neuropathies (PN) are heterogeneous nerve disorders; frequently rheumatic patients have neuropathic symptoms. In some rheumatic diseases (RD) PN are secondary to nerve compression while others are related to metabolic abnormalities, inflammation or vasculitis. Our aim was to explore the frequency of neuropathic symptoms with three neuropathy questionnaires (NQ) and nerve conduction studies (NCS) in RD. METHODS: This is a cross-sectional study in patients with any RD attending for the first time to a rheumatology outpatient clinic. We included all patients who accepted to participate and who answered three NQ and received a physical evaluation. Twenty patients were randomly selected to perform NCS and 10 healthy subjects were included as controls. The topographic diagnoses were: mononeuropathy, multiplex mononeuropathy, and/or polyneuropathy. STATISTICAL ANALYSIS: descriptive statistics (mean, median, standard deviation, interquartile range and frequency, odds ratios and Pearson correlation test). RESULTS: One hundred patients and 10 healthy subjects were included. Sixty-nine were female, mean age 40.6 ± 15.7 years. Rheumatic diagnoses were: systemic lupus erythematosus (26%), rheumatoid arthritis (16%), gout (14%), and osteoarthritis (11%). Fifty-two patients had neuropathic signs during physical examination and 67% had positive questionnaires with variable scores among several RD. Abnormal NCS was reported in 14 patients (70%): 6 (42.8%) median nerve mononeuropathies, 4 (28.5%) multiplex mononeuropathies and 4 (28.5%) polyneuropathies. None of the healthy subjects had neuropathy (NQ, physical evaluation, or NCS). Risk of being NCS positive is higher when the patients were NQ positive. CONCLUSION: PN has variable distribution and high frequency in patients with RD; NQ+ increases the risk of presenting NCS+ for PN.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".