The Development of Regenerative Medicine in Russia and in the World: Leading Researchers and Technological Drivers
Bibliographic record
Abstract
An assessment was made of the current level of development of the thematic area of «regenerative medicine» in Russia and in the world based on an analysis of the list of methods of cell, gene therapy and tissue engineering officially approved by the regulatory authorities of the world as of November 2022, as well as an analysis of the global publication and patent portfolio. It is shown that the practical result of the 20-year development of regenerative medicine was the creation and approval of 70 products and technologies, of which 12 are methods (products) of cellular immunotherapy, 11 are gene therapy products, 17 are cell therapy products, 8 are cord blood therapy methods, 22 – products of tissue engineering. The United States is the leader in terms of the number of regenerative medicine technologies and products approved by regulatory authorities for the use of technologies and products in healthcare practice – 32 methods and products, the second position is occupied by the EU (21), followed by the Republic of Korea (16), Japan (10), Canada (7). 3 technologies and products were approved in India, China and Australia, 2 in Singapore, 1 in New Zealand. During the observation period, only one gene therapy product was recalled (ZYNTEGLO in the EU in 2021). The country that makes the most significant contribution to research and development in the field of regenerative medicine is the United States, which has a national publication portfolio that is at least three times larger than the top 5 countries in this topic area (China, Japan, UK and Italy). The Russian Federation ranks 17th with the total number of publications in the national portfolio. The performed scientometric analysis made it possible to identify the most developed scientific areas in the Russian Federation related to the field of regenerative medicine.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".