Oocytes from younger women with increased serum FSH are superior to those from older women with hypergonadotrophism
Bibliographic record
Abstract
Dear Sir, We thank Dr Check for his interest in our study and the issues he raised in his letter. Firstly, Dr Check raised the issue whether young women are protected from the adverse effects of reduced ovarian reserve. He made reference to a study produced in 1997 (Check et al., 1998), where the authors evaluated pregnancy and on‐going pregnancy rates in a small number of women (n = 45) with raised basal FSH levels treated without the use of assisted reproductive technology (ART). It is fundamentally incorrect to compare data from IVF‐treated patients with those from the general subfertility population, when multifollicular development is not as important for achieving a pregnancy. Furthermore, no distinction was made in that study (Check et al., 1998) between patients ≤30 years and those between 31–39 years. In a larger study, life table analysis was used to evaluate the effect of age on pregnancy rates achieved in a group of women with reduced ovarian reserve, confirmed by an abnormal clomiphene challenge test (Scott et al., 1995). The study showed no age‐related differences in pregnancy rates, which were similarly low in all age groups studied. Within the field of IVF, in addition to our study, many studies (Scott et al., 1989; Toner et al., 1991; Margarelli et al., 1996) have emphasized the importance of ovarian age, over chronological age alone, in predicting treatment outcome.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".