Evaluating influence of the genotypes in the follicle-stimulating hormone receptor (FSHR) Ser680Asn (rs6166) polymorphism on poor and hyper-responders to ovarian stimulation: a meta-analysis
Bibliographic record
Abstract
BACKGROUND/AIMS: Reported associations of controlled ovarian hyperstimulation response (COH) with genotypes of the Ser680Asn (N680S) polymorphism in the follicle stimulating hormone receptor (FSHR) gene have conflicting results. METHODS: PubMed and Embase databases were searched for studies that investigated the N680S polymorphism in the FSHR gene in COH. Parameters used to examine ovarian response were poor and hyper-responses to COH. Using the meta-analytic approach, we estimated ovarian response risk (odds ratio [OR] with 95% confidence intervals) according to genotype. RESULTS: Our findings showed that SS genotype carriers were most likely to be poor responders (OR 1.61, p = 0.08) compared to the NN and NS genotypes which showed no associations (OR 0.93-0.95, p = 0.75-0.78). Heterogeneity of these pooled ORs warranted examining its sources. We detected outlying studies in each of the three N680S genotypes. Omitting these outliers erased the heterogeneity of the recalculated pooled outcomes. It also materially altered the SS effects where carriers became slightly unlikely to be poor responders (OR 0.90, p = 0.52). The S allele carrier effect was modulated for poor responders (OR 1.24, p = 0.39) in the Non-Hispanic Caucasian (NHC) subgroup. The likelihood of the S allele carriers (OR 1.47, p = 0.02) and the unlikelihood of the N allele carriers (OR 0.64, p = 0.007) were significant in our hyper-response findings. Confined to NHC retained significance of the S allele effects (OR 1.57, p = 0.01) but not among the N allele carriers (OR 0.68, p = 0.18). CONCLUSIONS: In summary, this is a meta-analytical confirmation of the FSHR SS genotype role in COH response. Hyper-responder analysis strengths lie on the non-heterogeneity and robustness of its results. Non-robustness and heterogeneity of the poor-responder results compose its limitations. Thus, poor response findings probably require caution as to the interpretation as a susceptibility marker for ovarian response.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.050 |
| 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".