Pre-Implantation Genetic Screening Techniques: Implications for Clinical Prenatal Diagnosis
Bibliographic record
Abstract
Chromosomal aneuploidy is responsible for a significant proportion of pregnancy failures, whether conceived naturally or through in vitro fertilization (IVF). In an effort to improve the success rate of IVF, screening embryos for aneuploidy - or pre-implantation genetic screening (PGS) - has been proposed as a means of ensuring only euploid embryos are selected for transfer. Early PGS approaches were based on fluorescence in situ hybridization testing, and have been shown not to improve live birth rates. Recent developments in genetic testing technologies - such as next-generation sequencing and quantitative polymerase chain reaction, coupled with embryo biopsy at the blastocyst stage - have shown promise in improving IVF outcomes, but they remain to be validated in adequately powered, prospective randomized trials. The extent to which IVF with PGS lowers the a priori risk of aneuploidy in ongoing pregnancies so conceived has been poorly described, rendering it difficult to incorporate the potential benefit of PGS into existing prenatal aneuploidy screening regimens such as cell-free DNA testing or conventional combined nuchal translucency and maternal biochemistry assessment. Further data on the sensitivity and specificity of various forms of molecular PGS testing would improve our understanding of the effectiveness and accuracy of these technologies. This, in addition to further research into methods of risk combination and assessment, would allow us to help our patients make better- informed decisions about whether or not to proceed with invasive diagnostic tests.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".