The role of sperm aneuploidy as a predictor of the success of intracytoplasmic sperm injection?
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: We present the first powered prospective study to assess whether sperm aneuploidy can predict the outcome of ICSI. METHODS: Our null hypothesis was that aneuploidy rates (AR) are identical in men who achieve successful (Group A) and unsuccessful (Group B) ICSI outcome. A power calculation yielded a sample number of 56 to achieve 80% power to reject our hypothesis at the 5% significance level. Samples for testing were obtained on the day of embryo transfer and tests were performed on raw pre-preparation samples. Sperm AR of chromosomes 13, 18, 21, X/Y were assessed using fluorescence in-situ hybridization (FISH) techniques (mean of 1223 sperm). RESULTS: There was no significant difference in any patient, seminal, cycle or laboratory characteristic between groups that may have affected outcome. Total AR (2.37 versus 1.18%, P = 0.01), as well as AR of chromosomes 18, X/Y and 18 + X/Y (1.48 versus 0.67%, P = 0.005) were significantly higher in Group B compared with Group A. Regression analysis confirmed these differences to be independent of other variables and showed a 2.6-fold change in odds of achieving a pregnancy for every 1% change in total AR. CONCLUSIONS: Our findings confirm a potential role for aneuploidy testing in the work-up of ICSI patients as a predictor of success, as well as in future genetic counselling. If confirmed, there may also be a place for a study of preimplantation genetic screening to improve ICSI success in men found to have high AR and ICSI failure.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it