<i>In vitro</i> maturation with letrozole priming: Can it be a solution for patients with cancerophobia? A pilot study
Bibliographic record
Abstract
OBJECTIVE: maturation (IVM) as compared with follicle-stimulating hormone (FSH) priming. MATERIALS AND METHODS: fertilization attempts (n=8). Forty-two patients received FSH priming and 21 patients received letrozole priming. RESULTS: The patients who had FSH or letrozole priming were statistically similar with respect to age, body mass index, duration of infertility, basal antral follicle count, serum anti-Müllerian hormone levels, and IVM indications (p>0.05 for all). When compared with the FSH priming group, the number of germinal vesicle oocytes, metaphase II and fertilized oocytes were significantly higher (p=0.003, p=0.001, and p=0.016, respectively), but the number of metaphase I oocytes was significantly lower in the letrozole priming group (p=0.002). The patients who received FSH and letrozole priming had statistically similar rates of implantation (33.3% vs 37.0%, p=0.709), clinical pregnancy (31.5% vs 33.3%, p=0.848), twinning (1.9% vs 3.7%, p=0.611), and live birth (24.1% vs 29.6%, p=0.682). CONCLUSION: Potential indications for IVM include patients with increased risk for OHSS and contraindication for hyperestrogenism. Aromatase inhibitors can be used to preserve the fertility of patients with estrogen-sensitive cancers. Letrozole priming appears to be an efficient approach in patients who undergo IVM, with likely less cost than FSH priming.
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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 | 0.001 |
| 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) | 0.002 | 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".