Association between serum uric acid levels and myasthenia gravis: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Serum uric acid (UA) levels may be involved in the development of myasthenia gravis (MG) by inhibiting oxidative stress, but the relationship remains unclear. This meta-analysis aimed to assess the association between serum UA levels and MG patients. METHODS: According to the established protocol, researchers searched 9 databases for studies on UA levels in MG patients, assessed using the Newcastle-Ottawa Scale. Heterogeneity was evaluated with the I2 statistic and chi-square test. Publication bias was analyzed using funnel plots and Egger test. RESULTS: This meta-analysis included 9 case-control studies from China, with 2112 participants (955 MG patients, 1157 healthy controls). All studies had Newcastle-Ottawa Scale quality scores of 7 or above. Results showed significantly lower serum UA levels in MG patients compared to controls (I2 = 58%, mean difference [MD]: -43.86, 95% confidence interval [CI]: [-54.98, -32.74], P < .00001). Age differences were identified as a source of heterogeneity, confirmed by subgroup analysis. Subgroup analyses showed that in age-comparable groups, MG and healthy controls had lower heterogeneity in UA levels (I2 = 18%, MD: -36.57, 95% CI: [-44.62, -28.50], P < .00001), and in age-disparate groups (I2 = 0%, MD: -73.78, 95% CI: [-94.13, -53.44], P < .00001). Gender analyses showed UA levels in men (I2 = 69%, MD: -60.29, 95% CI: [-81.75, -38.83], P < .00001) and women (I2 = 1%, MD: -29.80, 95% CI: [-38.03, -21.57], P < .00001). CONCLUSION: Lower serum levels of UA are associated with an increased risk of MG, although further large-scale, well-controlled studies are needed to confirm the potential clinical relevance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".