Harnessing the promises of cell therapy, gene therapy, and regenerative medicine in Québec, Canada
Bibliographic record
Abstract
The last decade has witnessed tremendous progress in the fields of cell and gene therapy and regenerative medicine (CGT/RM). However, these advances came with their own challenges and opportunities, which may vary among jurisdictions depending on infrastructures, laws, regulations, access to funding, and socioeconomic, political and cultural aspects. In September 2023, the Québec cell, tissue and gene therapy network (ThéCell) and Héma-Québec held a symposium on the opportunities and challenges of CGT/RM in Québec, Canada. We collected and synthetized the views of scientists, CGT/RM manufacturers, business representatives, and experts in ethics and law, pharmacoeconomics, and regulatory affairs expressed during and after this symposium. Comments were grouped into seven strategic orientations: (1) a framework should be built for the development of CGT/RM products based on principles that will enable fair and timely access for all; (2) governments should spur private and public research investments in CGT/RM; (3) the skillsets of developers should be mobilized to foster the development and production of CGT/RM products in the academic and industrial settings, and the training of Québec's workforce could be better aligned with industry and population needs to facilitate the industrialization of the sector, with the aim of reducing production costs and improving accessibility to patients; (4) Québec should ensure that the province's production and healthcare capacity is aligned with current and future needs in CGT/RM products, considering the rapidly evolving landscape of CGT/RM; (5) regulatory awareness may be improved among developers through outreach approaches and early consultations; (6) the regulations governing the development of CGT/RM could be streamlined and adapted to the needs of these emerging products; (7) ongoing efforts to reform the clinical reimbursement framework could be continued in Québec's public, single-payer healthcare system. This symposium provided guidance addressing current limitations and taking advantage of opportunities in CGT/RM in Québec. These considerations should help guide the development of new policies for CGT/RM products in Québec, keeping with principles of universal healthcare in Canada. We surmise that other jurisdictions face similar challenges, and the global CGT/RM community could benefit from these shared experiences to promote the optimal development and access to these promising therapies.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".