Bibliographic record
Abstract
e18339 Background: Despite high cure rates achieved in high-income countries, outcomes for children with Burkitt lymphoma (BL) in most low- and middle-income countries (LMICs) remain suboptimal. Perceptions of high cost and resource intensity remain political barriers to the prioritization of BL and other childhood cancer treatment programs in many LMIC health systems. Little to no knowledge exists of the actual cost and cost-effectiveness of treating paediatric cancers in LMICs. To improve outcomes for children with BL, the Uganda Cancer Institute implemented a comprehensive BL treatment program in 2012. Drawing on centralized patient-level data, we undertook an economic evaluation of the program to ascertain the cost-effectiveness of BL therapy in a specific LIC setting. Methods: We compared the treatment of BL (local standard) to usual care (no care), in a cohort of 215 patients treated between 2012 and 2015. Costs included direct, indirect healthcare, and indirect patient costs. Our primary measure of effectivenesswas overall survival (OS). Patient outcomes were determined through electronic chart abstraction. The cost per DALY averted was calculated using WHO-CHOICE methodology and compared to standard definitions of cost-effectiveness. Results: The 2-year OS with treatment was 53% (95% CI, 43% to 62%). Nine percent of patients abandoned therapy. The cost per DALY averted in the treatment group was US$78. Cumulative estimate of national DALYs averted through treatment was 11,046 years, and the total national cost of treatment was US$755,216. The ratio of cost per DALY averted to per capita gross domestic product (GDP) was 0.12, reflecting a very cost-effective intervention. Conclusions: This study demonstrates that treating BL with locally tailored protocols is very cost-effective relative to per capita GDP in Uganda. Studies of this kind will furnish crucial evidence to assist policymakers prioritize the allocation of LMIC health system resources among NCDs, including childhood cancer.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".