Prediction of ovarian hyperstimulation syndrome in women undergoing in vitro fertilization: A systematic review
Bibliographic record
Abstract
Background: Ovarian hyperstimulation syndrome (OHSS) is a significant complication of controlled ovarian stimulation in assisted reproductive technologies (ART). Identifying predictors of OHSS is important to optimize patient outcomes and prevent severe complications. This systematic review aimed to discuss evidence on predictive markers of OHSS in women undergoing in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) cycles.Methods: Following the PRISMA guidelines, electronic searches conducted in PubMed, Scopus, Web of Science, and Google Scholar databases. Keywords related to OHSS prediction and IVF were used, restricted to English-language full-text articles published between 2009 and 2024. Eligible studies included original prospective, retrospective, observational, or cohort studies investigating predictors of OHSS. Twelve studies were included in the qualitative synthesis.Results: Antral follicle count (AFC), anti-Müllerian hormone (AMH) levels, serum estradiol (E2) concentrations on the day of hCG administration, number of follicles, and number of retrieved oocytes emerged as the most evaluated predictors. High AFC, elevated AMH, and increased E2 levels were associated with development of OHSS. Several studies developed predictive models, including nomograms, with good discriminatory performance (AUC 0.70–0.85). Intrafollicular melatonin concentrations and coagulation factors were also studied.Conclusion: AFC, AMH, and E2 levels are reliable predictors of OHSS risk in IVF/ICSI cycles. Integrating traditional markers with emerging predictive tools allow earlier identification of high-risk patients, and enable stimulation protocols and improved clinical outcomes.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".