Basal serum luteinizing hormone, total testosterone, and free testosterone levels do not impact IVF outcomes in patients with polycystic ovary syndrome
Bibliographic record
Abstract
Objective: To assess the influence of basal serum levels of luteinizing hormone (LH), total testosterone (TT), and free testosterone (FT) on in vitro fertilization (IVF) success rates in patients with polycystic ovary syndrome (PCOS). Material and Methods: A retrospective cohort analysis of PCOS patients who underwent freeze-all, gonadotropin releasing hormone (GnRH) antagonist IVF protocols from January 2013 to December 2019. Patients were grouped based on median basal serum levels of LH, TT, and FT to compare their IVF outcomes. Results: A total of 76 women with PCOS diagnosed as per the 2003 Rotterdam criteria were included. When analyzed by LH levels, groups had similar baseline characteristics except for higher mean ± standard deviation TT (1.4±0.9 vs. 1.9±0.9 nmol/L, p=0.02) and FT (0.6±0.5 vs. 0.9±0.5 nmol/L, p=0.03) in the elevated LH group. However, clinical pregnancy rates (CPR) (34.2% vs. 44.7%, p=0.35) and live birth rates (LBR) (21.0% vs. 31.6%, p=0.29) were not different. The group with lower TT had more previous pregnancies (0.9±1.2 vs. 0.3±0.7, p=0.02) and shorter infertility duration (2.3±2.0 vs. 3.7±2.7 years, p=0.04), but again CPR (46.8% vs. 42.8%, p=0.90) and LBR (37.5% vs. 25.7%, p=0.33) were similar. FT analysis revealed no significant differences in CPR (48.2% vs. 36.7%, p=0.36) and LBR (23.2% vs. 37.9%, p=0.22) despite higher TT (1.1±0.4 vs. 2.2±1.1 nmol/L, p<0.001) and LH (6.1±3.8 vs. 11.2±7.2 IU/L, p<0.001) in the high FT group. Conclusion: Basal serum levels of LH, TT, and FT did not significantly affect IVF outcomes in patients with PCOS using GnRH antagonist, freeze-all protocols.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".