Ovarian response in natural cycle, minimal stimulation and conventional IVF protocols in relation to anti-mullerian hormone level – A multi-center study
Bibliographic record
Abstract
BACKGROUND: In addition to conventional in-vitro-fertilization IVF (cIVF) alternative protocols such as natural cycle (NC-) and minimal stimulation (Min stim-) protocols allow individualized IVF and oocyte freezing treatments. The objective was to develop a model to predict ovarian response in relation to different stimulation protocols and anti-mullerian hormone (AMH) levels. METHODS: International multi-centre retrospective cohort study including 1504 NC-IVF, 1287 Min stim IVF and 2048 cIVF cycles performed 01.2022-03.2023. Min stim protocols consisted of oral compounds such as low dose clomiphene citrate or aromatase inhibitors alone or in combination with low dose gonadotropins. Negative binomial regression models were used to assess the effect of protocol and AMH level on the number of oocytes and secondarily on zygotes. Predictions of outcomes were derived across protocols and different AMH categories (<1, ≥1-<2, ≥2 ng/ml). RESULTS: AMH is an effective predictor of the number of oocytes in relation to IVF protocol. Number of oocytes increase in protocols including gonadotropins, but not in protocols with only oral compounds. Effect of stimulation increased with increasing AMH level but was less pronounced with low AMH level. For example, if AMH is < 1 ng/ml, the predicted number of oocytes is 0.85 and 4.04 in the NC- and cIVF protocols, whereas the difference is twice as large with AMH ≥ 2 ng/ml with predicted values of 0.83 and 10.54, respectively. CONCLUSION: Based on AMH level, Min stim-protocols can be individualized by selecting specific stimulation protocols according to the predicted ovarian response, especially in cases with low ovarian reserve.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".