Assessment of the Ovarian Reserve by Serum Anti-Müllerian Hormone in Rheumatoid Arthritis Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The ovarian reserve has been reported to be diminished in patients with rheumatoid arthritis. However, these results are still controversial. Anti-Müllerian hormone (AMH) is considered a reliable biomarker for the ovarian reserve. We thus performed a meta-analysis to evaluate the AMH levels and the effect of DMARDs on the ovarian reserve in rheumatoid arthritis patients. METHODS: PubMed, EMBASE, the Cochrane Library, and 2 Chinese databases (CNKI and Wanfang database), up to September 2021, were searched for relevant studies. The Newcastle-Ottawa scale (NOS) was used to assess the quality of the included studies. Pooled standard mean difference (SMD) with 95% confidence intervals (CIs) were determined with the random-effects model. The heterogeneity was described by I2 statistic and p value from the Cochrane Q test. RESULTS: Eight eligible studies (679 patients and 1,460 controls) were included in the meta-analysis. Compared with healthy control, the AMH levels in RA patients were significantly lower with the pooled SMD of -0.40 (95% CI: -0.66 to -0.14). However, in comparison of AMH with and without DMARD treatment, there was no significant difference with the pooled SMD of -0.1 (95% CI: -0.39 to 0.19). CONCLUSION: The results indicated that there was an increased risk of ovarian failure in RA patients and which is not related to DMARD treatment.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".