Risk factors for ovarian hyperstimulation syndrome: relevance of the number of follicles, serum estradiol levels and the number of oocytes collected
Bibliographic record
Abstract
Dear Sir, We read with interest Dr Bodri's editorial on our recent report ‘Severe early ovarian hyperstimulation syndrome (OHSS) following GnRH agonist trigger with the addition of 1500 IU hCG’ (Bodri, 2013; Seyhan et al., 2013). However, we did question some of his comments. While Dr Bodri describes all other studies evaluating OHSS risks using this protocol as containing ‘normo-responders’ or just ‘high responders’ he calls our patients ‘very high responders’ and ‘not suitable candidates for the Humaidan protocol (Bodri, 2013). Dr Bodri's opinion seems to be solely based on our collecting a higher number of oocytes compared with the study of Humaidan (2009). Although the number of oocytes collected is a well-known risk factor for OHSS, there are other risk indicators including serum estradiol levels and the number of growing follicles (Papanikolaou et al., 2006). While serum estradiol levels are determined using standardized techniques around the world, and even physicians and technicians with limited experience can count follicles, the number of oocytes collected depends on the experience of both the physician and the embryologist. Experience in aspiration of small follicles and in identifying oocytes surrounded with sparse cumulus cells is especially important for oocyte collection from women with polycystic ovaries, which contains numerous small follicles besides larger ones. We think the number of collected oocytes is more likely to vary between clinics and is a less reliable indicator of ovarian response than serum estradiol levels or the number of growing follicles in this context.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".