True empty follicle syndrome is a subtype of oocyte maturation abnormalities
Bibliographic record
Abstract
Objective: To review the outcomes of in vitro maturation (IVM) and in vitro fertilization (IVF) in women with empty follicle syndrome (EFS). The study evaluated the genetic underpinnings of EFS by analyzing mutations. Materials and Methods: This retrospective case series involving 17 women with EFS over at least 2 IVF cycles was conducted. The study also employed whole-exome sequencing to analyze the genetic mutations. The treatment approaches included letrozole-primed IVM, follicle-stimulating hormone (FSH)-human chorionic gonadotrophin (hCG)-primed IVM, and conventional IVF. Results: The average female age was 31.5±4.6 years, and the duration of infertility was 7.3±3.5 years. Four patients underwent IVF. IVM oocyte collections yielded oocytes in 12 of 13 subjects. Of these, 75% (9/12) yielded MII oocytes after 48 h of IVM media incubation. Six subjects had fertilized embryos, resulting in a 40.9% intracytoplasmic sperm injection (ICSI) fertilization rate (9 embryos/22 MII oocytes). Genetic analysis revealed mutations in seven patients. This study demonstrated the partial efficacy of letrozole-primed IVM plus growth hormone and FSH-hCG primed IVM protocols. No pregnancies or live births were recorded after IVM. One ongoing pregnancy post-IVF and one spontaneous live birth were observed. Conclusion: Inter-cycle variabilities were observed in women with oocyte maturation abnormalities (OMAs). Almost all patients with EFS had oocytes collected during IVM following IVF. These oocytes have limited potential for maturation, fertilization, and live birth, as demonstrated by the low rates observed after IVM culture and ICSI. These conditions are observed in OMAs due to defects in the oocyte machinery. The proposed flowchart provides a comprehensive classification approach for various forms of EFS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".