Effect of repeated assisted reproductive technology on ovarian response
Bibliographic record
Abstract
PURPOSE OF REVIEW: Based on current rates of success, many infertile couples who desire pregnancy have to undergo repeated cycles of assisted reproductive technology. Concern has been raised that repeated cycles of assisted reproductive technology may have a detrimental effect on future ovarian response and function, as well as pregnancy. This review summarizes current knowledge of the effects of repeated assisted reproductive technology, highlighting recent publications. RECENT FINDINGS: The available published evidence so far indicates that the follicular response and the number of oocytes retrieved appears to be maintained with repeated treatment and the only significant decline in ovarian response is because of an increase in female age. Similarly pregnancy and live birth rates decline to a small degree only up to cycle 3 or 4, with increasing female age again being the prime determinant. Encouraging patients to undertake repeated treatment without undue delay leads to improved cumulative rates of pregnancy and live birth. Current evidence does not indicate that ovarian stimulation leads to an increased risk of ovarian malignancy. SUMMARY: Couples should be counselled from the outset that assisted reproductive technology treatment is a continuum and a number of treatment cycles may be necessary. At present, there is little indication that repeated cycles have a detrimental effect on ovarian function, although the outcome of further research is awaited.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".