Young age does not protect against the adverse effects of reduced ovarian reserve—an eight year study
Bibliographic record
Abstract
BACKGROUND: Ovarian reserve significantly influences IVF outcome. Low response to ovarian stimulation due to reduction of ovarian reserve is occasionally encountered in young women. The aim of this study was to evaluate the outcome of IVF treatment in young patients with reduced ovarian reserve. METHODS AND RESULTS: Between January 1993-2001, 762 consecutive patients satisfied the definition of reduced ovarian reserve (raised early follicular phase FSH or gonadotrophin stimulation cycles where three or fewer oocytes were retrieved after routine FSH stimulation) and were included in the study. They were classified into three age groups: young (< or = 30 years), intermediate (31-38 years) and older (>38 years). The three age groups were similar with respect to basal (day 3) serum FSH and estradiol concentrations, cause of infertility and number of previous treatment cycles. Implantation (13, 9.6 and 9.8%), clinical pregnancy (11.8, 10.2 and 10%) and live birth (7.4, 7.3 and 6.8%) rates were not significantly different in the three age groups respectively (P > 0.05). CONCLUSION: This study shows that younger patients with reduced ovarian reserve have a poor outcome of IVF treatment similar to their older counterparts. Such information may be helpful in counselling these patients who otherwise might anticipate an outcome related to their chronological age.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".