Overcoming barriers to facilitate the regulation of multi-centre regenerative medicine clinical trials
Bibliographic record
Abstract
In the context of regenerative medicine and cellular therapies, the treatment under study often targets a less common disease or condition for which recruitment of a large number of research participants at any given site is challenging, if not impossible. One way to overcome this challenge is with a multi-centre clinical trial. This manuscript first aims to briefly outline the existing ethical, legal and social implications as well as the regulatory frameworks associated with multi-centre regenerative medicine clinical trials. Second, it considers the regulatory limitations and barriers surrounding the initiation of such trials in Canada, the USA and Europe. Third, it concludes with a set of recommendations for facilitating multi-centre clinical trials, at both national and international levels.
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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 | 0.380 | 0.379 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".