Preimplantation genetic diagnosis (PGD), a collaborative activity of clinical genetic departments and IVF centres
Bibliographic record
Abstract
Preimplantation genetic diagnosis (PGD) requires the combined efforts of geneticists and workers in the field of reproductive medicine. This was studied on the basis of a questionnaire, sent to 35 members of the PGD Consortium of the European Society of Human Reproduction and Embryology (ESHRE). A reply was obtained from 20 centres. They represent the majority of activities in the field of PGD in the world. It is obvious that many of the activities (in vitro fertilisation, embryo culture and biopsy) take place in IVF units while others (counselling and diagnosis) are the responsibility of genetic diagnostic centres. The distances between both units vary considerably. In all but one centre sex determination is offered. Aneuploidy screening is offered in 13 out of 20 centres. PGD of translocations and other structural chromosome abnormalities is offered in all but one centre. The number of monogenic diseases offered varies considerably. In comparison to prenatal diagnosis PGD is more expensive. The majority of these costs are due to the IVF or ICSI procedure. The charges for PGD vary between about 600 euro and 4000 euro. In 16 out of 20 centres the parents to be must sign an informed consent form.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".