Government development of ‘made-in-Canada’ CAR-T cell immunotherapies: assessing cost, risk, access, and alternatives
Bibliographic record
Abstract
Chimeric Antigen Receptor T, or CAR-T cell immunotherapy, is a novel treatment that genetically engineers a patient's own T-cells to recognize and attack cancer cells.CAR-T therapeutics have been available in the United States since May 2017, and in Canada since September 2018.As of July 1, 2024, six CAR-T products had been authorized for marketing by both the U.S. Food and Drug Administration (FDA) and Health Canada.One drug was later withdrawn from the Canadian market.Health technology assessment (HTA) is a prerequisite of the public reimbursement process in Canada and is conducted by the Canadian Drug Agency (CDA), formerly known as the Canadian Agency for Drugs and Technologies in Health (CADTH).The five commercially available CAR-T products were recommended for public reimbursement by CDA.However, the prices for these therapies exceeded the cost effectiveness threshold used by CDA and therefore its recommendations were conditional on pricing adjustment.CAR-T has been eligible for public funding under US Medicare since 2017.By contrast, as of July 1, 2024, only six of the 10 Canadian provinces have authorized CAR-T products for public reimbursement.Lack of public funding is a significant barrier to accessing CAR-T immunotherapy, in addition to several other obstacles to treatment affecting patient access.Federal and provincial governments have been reluctant to extend funding eligibility for commercial products, preferring instead to invest in public development of a made-in-Canada capacity for manufacturing CAR-T therapies.Alternative funding models could more efficiently and more immediately improve access using commercially available CAR-T therapies in Canada.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".