Free-androgen Index in Women with Polycystic Ovarian Syndrome: A Meta-Analysis
Bibliographic record
Abstract
Abstract Objectives Hyperandrogenism, a key feature of polycystic ovarian syndrome (PCOS), is caused by excess androgen secretion, most commonly of ovarian origin. Although the serum total testosterone (TT) levels have long been used as a traditional measure of hyperandrogenemia in women with PCOS, it is associated with many fallacies due to the fact that a component of TT is linked to sex hormone-binding globulin (SHBG). Recent research has discovered that measuring free testosterone levels and computing the free androgen index (FAI), which is a ratio of TT and SHBG, are better predictors of androgen excess in PCOS. The aim of this meta-analysis is to determine the association of FAI in diagnosing hyperandrogenism and its ability to discriminate PCOS from controls. Materials and Methods The publicly available databases PubMed, Scopus and Web of Science were searched using MeSH terms, ‘Polycystic Ovarian Syndrome’ OR ‘PCOS’ OR ‘PCOD’ AND ‘Testosterone’ AND ‘Sex Hormone Binding Globulin’ OR ‘SHBG’ to collect the full-text articles for the retrieval of related data of case–control and cross-sectional studies. The studies quality was assessed using the Newcastle–Ottawa scale, and a sub-group analysis and publication bias between the studies was evaluated by funnel plot. Statistical Analysis The R program (v4.0.3) and R packages ‘metafor’ and ‘dmetar’ were used for statistical analyses of quantitative data and the plots were generated using ‘ggplot2’ package through a comparison of pooled SMD by Egger's linear regression and Beggs-Mazumdar tests. Results Twenty-four studies involving 7,847 participants including 3,290 controls and 4,557 PCOS were included in the meta-analysis. The pooled data analysis of the included studies showed that the PCOS women had higher FAI than controls, with SMD of 1.56 (95%CI 1.08–2.04; p < 0.01). The publication bias was tested using a funnel plot and Egger's regression asymmetry test, which revealed no risk of publishing bias (p = 0.1727). Additionally, the sub-group meta-analysis of geographic region revealed that FAI levels were more significant in PCOS subjects of Asia and Europe, compared with the American region. Conclusion Overall, this meta-analysis indicates that FAI could be a reliable marker to differentiate PCOS patients from controls in Asian and European ethnicities.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.039 |
| Bibliometrics | 0.004 | 0.006 |
| 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".