Reasons for conducting preimplantation genetic diagnosis: choosingthe optimal model of legal regulation
Bibliographic record
Abstract
Objectives. The aim of this study is to find the most optimal model for providing access to preimplantation genetic diagnosis (PGD) based on generalization and study for existing foreign experience using countries that widely practice this type of diagnosis and have significant clinical experience and have passed the time-tested regulatory framework. Material. The legal acts and doctrinal sources of Austria, Belgium, Great Britain, Germany, Israel, Canada, France, Switzerland, and Japan are studied. Methods used: general philosophical, general scientific, private scientific, special (structural-legal, comparative-legal, formal-legal). Results. The advantages and disadvantages of each of the models of legal regulation of PGD (the medical model, the high-risk model and the model of non-interference) are evaluated, the conclusion is substantiated that it is advisable to build on the combination of these to determine the legal grounds for conducting PGD in the Russian Federation. Conclusions. It has been established that it is advisable to introduce elements of the medical model through the legal definition of lists of monogenic diseases and chromosomal abnormalities that are the basis for PGD, which will eliminate legal uncertainties in understanding and regulating this procedure and provide access to this type of diagnosis within the framework of IVF programs financed from funds Compulsory medical insurance. The implementation of elements of a high-risk model is proposed in conjunction with mechanisms for authorizing PGD for special cases, including the assessment of additional risks in the presence of a major serious genetic disease or indications for determining the risks of aneuploidy. It has been argued that a variable assessment of the conditions for conducting PGD should be provided normatively and include the possibility of proactively authorizing this type of diagnosis at the expense of patients for gene mutations with a relatively low risk of transmission, diseases with incomplete penetrance, and treatable genetic diseases (including, for example, cancer breast and ovarian cancer, some cardiogenetic disorders), etc.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".