Preimplantation genetic diagnosis for monogenic diseases: overview and emerging issues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Preimplantation genetic diagnosis (PGD) is an established reproductive option for couples at risk of conceiving a pregnancy affected with a known genetic disease, who wish to avoid an (additional) affected child, termination of pregnancy or recurrent miscarriages. Early technologies concentrated on different approaches to direct mutation testing for monogenic diseases using single cell PCR protocols, or sex selection by fluorescent in situ hybridization for X-linked monogenic disease. Development of multiplex fluorescent PCR allowed simultaneously testing of linked markers alongside the mutation test, increasing the accuracy by controlling for contamination and identifying allele drop-out. The advent of highly effective whole genome amplification methods has opened the way for new technologies such as preimplantation genetic haplotyping and microarrays, thus increasing the number of genetic defects that can be detected in preimplantation embryos; the number of cases carried out and the new indications tested increases each year. Different countries have taken very different approaches to legislating and regulating PGD, giving rise to the phenomenon of reproductive tourism. PGD is now being performed for scenarios previously not undertaken using prenatal diagnosis, some of which raise significant ethical concerns. While PGD has benefited many couples aiming to have healthy children, ethical concerns remain over inappropriate use of this technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.000 | 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