Falling estradiol levels as a result of intentional reduction in gonadotrophin dose are not associated with poor IVF outcomes, whereas spontaneously falling estradiol levels result in low clinical pregnancy rates
Bibliographic record
Abstract
BACKGROUND: Although estradiol levels remain an integral part of monitoring in most IVF programmes, the effect of falling estradiol on IVF outcome has not been adequately quantified. The objective of this study was to evaluate the effect of falling estradiol levels prior to hCG on IVF outcome. METHODS: This was a retrospective cohort study carried out in a university-based fertility clinic. A total of 112 IVF patients in whom estradiol levels fell prior to the administration of hCG were matched for age and year of treatment with 112 control IVF patients. IVF outcomes including oocytes retrieved, fertilization rate, embryos for transfer, and pregnancy rates were compared between the groups. RESULTS: Seventy per cent of women in the falling estradiol group experienced spontaneously falling estradiol levels. Spontaneously falling estradiol was associated with fewer oocytes retrieved (median 5 versus 8, P=0.001), increased rates of failed fertilization (18 versus 6%, P=0.018) and lower clinical pregnancy rates (12 versus 26%, P=0.012) compared to controls. Despite marked decreases in estradiol levels, IVF outcomes for patients whose estradiol levels fell as a result of deliberate protocol modification had similar fertilization and clinical pregnancy rates as controls. CONCLUSIONS: Subtle (<10%) spontaneous decreases in estradiol levels are associated with very poor IVF outcomes.
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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 | 0.005 |
| 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 | 0.001 |
| 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".