Institutional Profile: The International Society for Cellular Therapy: Evolving to Meet the Demands of the Regenerative Medicine Industry
Bibliographic record
Abstract
The International Society for Cellular Therapy is a global association driving the translation of scientific research to deliver innovative cellular therapies to patients. Established in 1992, its membership and leadership comprises world-class scientists, clinicians, technologists, biotech/pharma and regulatory professionals from 40 countries focused on preclinical and translational aspects of developing cell therapy products. The International Society for Cellular Therapy has evolved in alignment with the maturation of the field of cell therapy and regenerative medicine to create forums for discussion of shared concerns for commercialization of cell therapies and of development of consensus standards, recognizing that true commercialization depends upon the translational scientific community, the regional regulatory and policy institutions, and the technology support and capital investment from industry. It exists to facilitate the international work of many, to spawn new initiatives, and to synergize with other stakeholders to create the best outcome for the many patients across the world depending on the answers and improved health that cellular therapeutics will provide them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.040 |
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".