Medicine in the Digital Era: Legal Aspects of the Use of Cell and Gene Therapy in Foreign Countries
Bibliographic record
Abstract
The paper examines the experience of legal regulation of the use of cell and gene therapy products, including CAR-T technologies, in the Anglo-Saxon legal system. It is noted that a significant obstacle to the development of CAR-T therapy, as well as cell and gene therapy in general, is the absence in most countries of the world of comprehensive legal regulation of the use of such innovative methods of treating diseases. Currently, this problem is relevant for the Russian Federation, where cell and gene therapy drugs are actively being developed. The paper provides a detailed overview of the main relevant documents from Australia, the United States of America and Canada, analyzes specific cases illustrating successful law enforcement practice, and examines the mechanisms of self-regulation in the area under study. In conclusion, the authors formulate the key problems and ways to improve legal regulation as to cell and gene therapy drugs application in the Russian Federation. The authors recommend that the best practices of these foreign countries be used, taking into account its critical understanding for the development of appropriate regulatory regulation in the Russian Federation and integration associations with its participation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".