The role of sperm aneuploidy as a predictor of the success of intracytoplasmic sperm injection?
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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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".