Does daily co-administration of letrozole and gonadotropins during ovarian stimulation improve IVF outcome?
Bibliographic record
Abstract
BACKGROUND: For the last year we have been treating normal responders with gonadotropins and letrozole during the whole stimulation in order to improve response to FSH by increasing the intrafollicular androgen concentration, and to reduce circulating estrogen concentrations. The aim of this study was to compare the IVF outcome of normal responders treated with letrozole and gonadotropins during ovarian stimulation with patients treated with gonadotropins only. METHODS: A single centre retrospective cohort study of 174 patients (87 in each group). RESULTS: The age of the patients was comparable between the groups. Estradiol levels were significantly higher in the control group (6760 pmol/L vs. 2420 pmol/L respectively, p < 0.01), and the number of follicles ≥15 mm at the trigger day was significantly lower in the control group (7.9 vs. 10, p = 0.02). The number of retrieved oocytes (10 vs. 14.5, p < 0.01), MII oocytes (7.9 vs. 11.2, p < 0.01) and blastocysts (2.7 vs. 4.0, p = 0.02) was significantly higher in the study group. We found no significant differences in the cumulative pregnancy outcome between the two groups (65.2% vs 58.3% p = NS). CONCLUSIONS: We conclude that co-treatment with letrozole improves the IVF outcome in normal responders in terms of increased number of blastocysts obtained without increasing the pregnancy rate or the risk of OHSS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".