A Comparison of In Vitro Maturation and In Vitro Fertilization for Women With Polycystic Ovaries
Bibliographic record
Abstract
In Brief OBJECTIVE To establish the relative success of treatment by unstimulated in vitro maturation (IVM) of oocytes or stimulated in vitro fertilization (IVF) in women with polycystic ovaries undergoing assisted conception treatment. METHODS The case-control study included 107 IVM and 107 IVF cycles matched for age and cause of infertility. In vitro maturation patients underwent transvaginal recovery of immature oocytes during an unstimulated cycle, in vitro oocyte maturation, and fertilization. Those in the IVF group underwent ovarian stimulation after pituitary suppression. Embryos were transferred in the same cycle in both groups. Main outcome measures included numbers of mature oocytes and embryos produced, and rates of implantation, pregnancy, live birth, and complications. RESULTS In the IVM group after in vitro culture, 7.8 mature oocytes and 6.1 embryos were obtained per retrieval. With IVF, 12.0 mature oocytes (P < .01) and 9.3 embryos (P < .01) were obtained. The IVM pregnancy and live birth rates per retrieval were 26.2% and 15.9% compared with 38.3% and 26.2% for IVF (nonsignificant). The implantation rate of IVF-derived embryos was higher (17.1% versus 9.5%) than that for IVM (P < .01). There were 12 cases (11.2%) of moderate or severe ovarian hyperstimulation syndrome in IVF patients, compared with none in the IVM group (P < .01). CONCLUSION Our results suggest that for women with polycystic ovaries who require assisted conception, IVM is a promising alternative to conventional IVF treatment. Unstimulated in vitro maturation of oocytes in women with polycystic ovaries has lower implantation and pregnancy rates but less ovarian hyperstimulation than stimulated in vitro fertilization.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".