Prevalence of autoimmune thyroiditis in women with polycystic ovary syndrome: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Polycystic ovary syndrome (PCOS) is one of the women's most common endocrine disorders, which may be is associated with some autoimmune diseases such as autoimmune thyroiditis disorders (AIT). This study was performed with aim to evaluate the prevalence of autoimmune thyroiditis in women with PCOS by a systematic review and meta-analysis. Methods: This review study was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. The research documents were collected by searching Persian-language databases such as Magiran, Sid, Iranmedex, and English-language databases such as Web of Science, Pubmed Scopus, and Google Scholar from January 1 until August 1, 2022. All studies were evaluated by the Newcastle-Ottawa scale at the four levels of selection, comparison, exposure, and outcome. Data analysis was performed with comprehensive meta-analysis (CMA) software (version 3.1). Results: A total of 9 studies with 2290 participants were included in the meta-analysis. The prevalence of AIT in patients with polycystic ovary syndrome was 26.4% (95% CI: 21.3-32.2) and in healthy women was 11.7% (95% CI: 8.1-16.7). Subgroup studies demonstrated a higher prevalence of AIT in younger patients. Meta-regression analysis also showed a significant association between the study area and the prevalence of AIT. Conclusion: There is a significant association between the increased prevalence of AIT in patients with PCOS. This shows the importance of evaluating and monitoring thyroid function in patients with PCOS.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| 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 | 0.002 | 0.002 |
| 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".