Evaluating the impact of repeated ovarian stimulation cycles on follicle development in patients with suboptimal response
Bibliographic record
Abstract
OBJECTIVE: To assess if repeat ovarian stimulation improves follicle development after a canceled suboptimal cycle. METHODS: This retrospective cohort study included 162 patients who underwent two consecutive ovarian stimulation cycles between March 2018 and February 2024, with their initial cycle canceled due to the development of three or fewer follicles ≥14 mm. The primary outcome was the number of follicles ≥14 mm before ovulation triggering. RESULTS: The mean age was 36.9 years during the first cycle and 37.2 years during the second. The median antral follicle count (AFC) was 9.5, and the mean anti-Müllerian hormone (AMH) level was 1.4 ng/mL. Compared with the first cycle, the second cycle showed increased mean daily follicle-stimulating hormone dose (from 352.4 to 401.2 IU, P < 0.001), stimulation duration (from 8.9 to 10.3 days, P < 0.001), peak estradiol (from 1927.7 to 3976.5 pg/mL, P < 0.001), and endometrial thickness (from 8.8 to 9.4 mm, P = 0.035). The mean number of follicles ≥14 mm increased from 1.5 to 4.2 (P < 0.001), with 125 of 162 (77.2%) cycles showing improvement. Protocol changes occurred in 90/162 (55.6%) cycles and there was a higher dose in 44/162 (27.1%). The mean increase in follicles ≥14 mm was 1.96 (95% confidence interval [CI] 0.92-3.00) with the same protocol and dose, 3.70 (95% CI 2.24-5.15) with a higher dose, and 2.40 (95% CI 1.76-3.03) with protocol change. Patients with AMH <1 ng/mL or AFC <7 were less likely to improve. CONCLUSION: Most patients with an initial suboptimal response showed improved follicle development in subsequent cycles, particularly with protocol modifications or increased gonadotropin dosage. Patients with diminished ovarian reserve were less likely to improve and some experienced worse outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.000 |
| 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".