Can we eliminate severe ovarian hyperstimulation syndrome? Not completely
Bibliographic record
Abstract
Sir, It is with great interest that we read Dr Orvieto's article ‘Can we eliminate severe ovarian hyperstimulation syndrome?’ (Orvietto, 2005). Of course, as cases of spontaneous ovarian hyperstimulation syndrome (OHSS) have continued to be reported in the literature since the 1990 s (Ayhan et al., 1996) and possibly earlier (O'Loughlin and Brookes, 1987), we will never be able to completely eliminate OHSS. Nevertheless, Dr Orvietto and his co-workers should be commended on developing a clinical algorithm to reduce the risk of severe OHSS following ovarian stimulation. The use of GnRH agonists to induce a gonadotrophin surge in the place of hCG is not new (Buckett et al., 1998) and it is wrong to suggest that its use will almost totally eliminate severe OHSS. Reports in the early 1990s (Bentick et al., 1990; van der Meer et al., 1993) demonstrated that GnRH agonists do not prevent OHSS—even in non-conception cycles. We believe that immature oocyte retrieval and in vitro oocyte maturation (IVM) without any ovarian stimulation at all is a more reliable way to reduce the risk of OHSS in women with polycystic ovaries who are at risk and yet maintain a reasonable pregnancy rate (Chian et al., 2000). In women with normal ovulatory ovaries, the combination of natural cycle IVF with IVM (Chian et al., 2004) will similarly lead to a reduced risk of OHSS—even if GnRH agonists are substituted for hCG in ‘at risk’ women. In our experience so far, we have performed over 500 cycles of conventional IVM or natural cycle IVF combined with IVM without any cases of OHSS and have achieved clinical pregnancy rates of 30–40% in women aged <40 years of age. The only way to avoid iatrogenic OHSS is to avoid ovarian stimulation.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.036 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".