CellCAN: A Unique Enabler of Regenerative Medicine and Cell Therapy in Canada
Bibliographic record
Abstract
Regenerative Medicine and Cell Therapy (RMCT) is paving the way for the most innovative and promising medical breakthroughs of the 21st century. Indeed, its curative potential is immense and builds on the already proven benefits of stem cell transplantation. Successful and broad clinical implementation of RMCT, as well as reaping of its full social and economic benefits, is contingent on the resolution of a range of issues. The CellCAN network, a not-for-profit corporation, was created to tackle these challenges, gathering the key forces of the numerous Canadian organizations involved in basic research, assay development, manufacturing, clinical research, clinical trials, legal and ethical regulations, and policies, all working to move RMCT forward. CellCAN creates a national enterprise by bringing together a community of renowned researchers, industries, clinicians, funders and regulators, and aligning it with cell-handling facilities involved in processing cell products and other products for cell therapy clinical trials to ensure capacity and know-how for stem cell research and efficient execution of cell therapy clinical trials. CellCAN is uniquely positioned to accelerate the implementation of RMCT in Canada and disseminate novel developments and findings, thus significantly contributing to the world's knowledge in cellular therapeutics. As such, the CellCAN model could also serve as a useful benchmark to accelerate RMCT implementation in other countries.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".