Survival Outcomes for US and Canadian Patients Diagnosed with Hodgkin Lymphoma before and after Brentuximab Vedotin Approval for Relapsed/Refractory Disease: A Retrospective Cohort Study
Bibliographic record
Abstract
Cost-effectiveness analyses are required for therapies within Canada's universal healthcare system, leading to delays relative to U.S. healthcare. Patients with Hodgkin lymphoma (HL) generally have an excellent prognosis, but those who relapse after or are ineligible for transplant benefit from novel therapies, including brentuximab vedotin (BV). BV was FDA-approved in 2011 but not Canadian-funded until 2014. To assess the impact of access delays, we compared changes in survival for U.S. (by insurer) and Canadian patients in periods pre/post-U.S. approval. Patients were 16-64 years, diagnosed with HL in 2007-2010 (Period 1) and 2011-2014 (Period 2) from the U.S. SEER and Canadian Cancer Registries. Approval date (surrogate) was utilized as therapy was unavailable in registries. Kaplan-Meier survival curves and adjusted Cox regression models compared survival between periods by insurance category. Among 12,003 U.S. and 4210 Canadian patients, survival was better in U.S. patients (adjusted hazard ratio (aHR) 0.87 (95%CI 0.77-0.98)) between periods; improvement in Canadian patients (aHR 0.84 (95%CI 0.69-1.03) was similar but non-significant. Comparisons between insurers showed survival was significantly worse for U.S. uninsured and Medicaid vs. U.S. privately insured and Canadian patients. Given the increasingly complex nature of oncologic funding, this merits further investigation to ensure equity in access to therapy developments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".