Assessing Canadaʹs Health System Readiness for Complex Therapies - The Current and Future State of T-Cell Redirecting Therapies
Bibliographic record
Abstract
Background: Interventions are considered complex when a number of factors associated with their use contribute to their health system impact (i.e., costs and effectiveness). An emerging complex intervention is the use of T-cell redirecting therapy. These therapies change the behaviour of a patient’s T-cells to modify (usually amplify) an immune response. Feasible approaches to care delivery, or initiatives that may support the safe delivery of these therapies given their real potential for expansion were identified. In doing so, the purpose of this report is to identify alternative feasible approaches to care delivery, or initiatives that may support the safe delivery and access to care of complex therapies in the Canadian and other health systems; Methods: readiness for complex therapies was explored using a mixed-methods approach. Information was sought using a conventional content approach and based on semi-structured interviews (30–60 min) and deliberation across key informants including patient representatives (n=2), healthcare system leaders/ administrators (n=2), and healthcare providers (n=11). (3); Results: This discussion revealed several insights for the future of complex therapies that will require attention including the need for: organizational change leadership and a change management function; specialized programs of care and implementation of navigational tools and educational strategies directed to providers and patients; trans-parent processes of evaluation that adhere to good practices in health technology assessment and implementation science; improving data collection to measure the cost and impact of new complex interventions; novel approaches to financing.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".