REGULATING PREIMPLANTATION GENETIC DIAGNOSIS: THE CASE OF DOWN'S SYNDROME
Bibliographic record
Abstract
Preimplantation genetic diagnosis (PGD) involves the testing of embryos produced through in vitro fertilisation (IVF) or intracytoplasmic sperm injection. One or two blastomeres are excised from the embryo at the 6- to 8-cell stage, and a genetic analysis is conducted with probes to detect heritable genetic conditions. Most commonly, only ‘unaffected’ embryos will then be transferred to the uterus in the hope of initiating a pregnancy that in all likelihood will not be affected by the familial disorder or chromosomal anomaly tested for.1 PGD was originally developed in the late 1980s as an alternative to prenatal diagnosis (PND) for couples wishing to produce a genetically related child free of an undesired, heritable, genetic condition where at least one of the prospective parents is a known carrier.2 Given that it is possible, and in the opinion of some desirable3 to utilise PGD to select against Down's syndrome embryos in the context of IVF, is it appropriate for health care professionals to offer, and society to permit, the use of this technology for this purpose? What makes this condition so ‘serious’—in contradistinction to other ‘not-serious-enough’ conditions—that PGD testing for it is deemed an appropriate intervention.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.008 |
| 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".