Re-envisioning the value proposition for investment in cancer care
Bibliographic record
Abstract
The place of cancer within broader health system development highlights key contradictions and distortions. Although innovation in cancer care, spanning medicines, artificial intelligence, and radiation therapy, are advancing rapidly, these technologies can be costly and often provide marginal benefits. Lower-cost approaches, such as screening, patient navigation, and supportive care, remain underutilized, especially in developing health systems. Simultaneously, the financial burden of cancer exacerbates inequities, driving patients into poverty and straining under-resourced systems. To address these contradictions, we call for a re-envisioning of cancer care as a strategic investment within health systems by presenting 4 key transitions: (1) a shift in the predominant narrative of cancer control as a clinical problem to positioning it as a universal health system priority, with far-reaching societal and economic benefits; (2) a greater emphasis on how cancer care supports health system strengthening across the full continuum of services involved in cancer control; (3) a view on cancer treatment as a gateway for technology and systemic investments, showcasing the potential to generate and use cross-cutting improvements with benefits across clinical areas; and (4) the need to align cancer control with rigorous fiscal, economic, and operational planning to ensure that investments deliver broad and sustainable health system impacts. By embedding cancer control into health system development, governments can optimize resources, strengthen system resilience and close equity gaps while addressing current and future health challenges.
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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 | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.012 |
| 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".