Individualization of the starting dose of follitropin delta reduces the overall OHSS risk and/or the need for additional preventive interventions: cumulative data over three stimulation cycles
Bibliographic record
Abstract
RESEARCH QUESTION: Is individualization of dosing with follitropin delta in sequential ovarian stimulation cycles an effective preventive strategy for ovarian hyperstimulation syndrome risk? If so, for which patients does an individualized strategy provide the greatest OHSS risk reduction and/or the need for additional preventive interventions? DESIGN: A secondary analysis of three ovarian stimulation cycles in IVF/intracytoplasmic sperm injection patients included in one randomized, assessor-blinded trial comparing two recombinant FSH preparations (ESTHER-1, NCT01956110), and a second trial in women undergoing up to two additional cycles (ESTHER-2, NCT01956123). Of 1326 women (aged 18-40 years) randomized and treated with follitropin delta or alfa in cycle 1, 513 continued to cycle 2 and 188 to cycle 3. Follitropin delta and alfa doses were maintained/adjusted according to ovarian response in the previous cycle. RESULTS: Individualized dosing with follitropin delta significantly reduced moderate/severe OHSS and/or preventive interventions (P=0.018) versus conventional dosing with follitropin alfa in patients undergoing up to three ovarian stimulation cycles. The greatest benefit was observed in patients in the highest anti-Müllerian hormone (AMH) quartile (P=0.012). On evaluating separately, individualized dosing with follitropin delta significantly lowered the incidences of moderate/severe OHSS (P=0.036) and preventive interventions (P=0.044) versus follitropin alfa. CONCLUSION: An individualized follitropin delta dosing regimen decreased the risk of moderate/severe OHSS as well as the incidence of preventive interventions versus a conventional follitropin alfa regimen. An analysis per AMH quartile indicated that these statistically significant differences are driven mainly by patients with the highest pretreatment AMH levels.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".