Impact of Systemic Delays for Patient Access to Oncology Drugs on Clinical, Economic, and Quality of Life Outcomes in Canada: A Call to Action
Bibliographic record
Abstract
Canada has one of the most complex and rigorous drug approval and public reimbursement processes and is, unfortunately, one of the countries with the longest delays in drug access. To assess the overall impact of systemic delays in access to cancer therapy, a targeted literature review (TLR) was performed to identify studies associated with the clinical, economic, and quality of life impacts of delayed access to oncology drugs. Using MEDLINE/PubMed databases and snowballing, four unique records met the eligibility criteria. Results revealed that clinical outcomes were the most impacted by systemic delays in access to oncology drugs (e.g., life years lost, overall survival, and progression-free survival). The four articles retrieved by the TLR specifically illustrated that a substantial number of life years could potentially be saved by increasing systemic efficiency regarding the development, approval, and reimbursement processes of new drugs for advanced malignancies. It is imperative that initiatives are put in place to improve the performance and speed of Canadian drug regulatory and health technology assessment (HTA) processes, especially for new cancer therapeutics. The proposed solutions in this paper include better coordination between HTA and Canadian payers to harmonize coverage decisions, international collaborations, information sharing, and national standards for timeliness in oncology drug access.
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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 | 0.033 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".