Ethical dilemmas in prioritizing patients for scarce radiotherapy resources
Bibliographic record
Abstract
BACKGROUND: Radiotherapy is an essential component of cancer treatment, yet many countries do not have adequate capacity to serve all patients who would benefit from it. Allocation systems are needed to guide patient prioritization for radiotherapy in resource-limited contexts. These systems should be informed by allocation principles deemed relevant to stakeholders. This study explores the ethical dilemmas and views of decision-makers engaged in real-world prioritization of scarce radiotherapy resources at a cancer center in Rwanda in order to identify relevant principles. METHODS: Semi-structured interviews were conducted with a purposive sample of 22 oncology clinicians, program leaders, and clinical advisors. Interviews explored the factors considered by decision-makers when prioritizing patients for radiotherapy. The framework method of thematic analysis was used to characterize these factors. Bioethical analysis was then applied to determine their underlying normative principles. RESULTS: Participants considered both clinical and non-clinical factors relevant to patient prioritization for radiotherapy. They widely agreed that disease curability should be the primary overarching driver of prioritization, with the goal of saving the most lives. However, they described tension between curability and competing factors including age, palliative benefit, and waiting time. They were divided about the role that non-clinical factors such as social value should play, and agreed that poverty should not be a barrier. CONCLUSIONS: Multiple competing principles create tension with the agreed upon overarching goal of maximizing lives saved, including another utilitarian approach of maximizing life-years saved as well as non-utilitarian principles, such as egalitarianism, prioritarianism, and deontology. Clinical guidelines for patient prioritization for radiotherapy can combine multiple principles into a single allocation system to a significant extent. However, conflicting views about the role that social factors should play, and the dynamic nature of resource availability, highlight the need for ongoing work to evaluate and refine priority setting systems based on stakeholder views.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".