First Ready, First to Go: Ethical Priority-Setting of Allogeneic Stem Cell Transplant at a Major Cancer Centre
Bibliographic record
Abstract
Medical advancements have now made it possible to provide allogeneic stem cell transplantation (allo-SCTs) to older patients and use stem cells from less well-matched donors. This has resulted in access to a life-saving modality for a greater number of patients with imminent life-threatening illnesses. However, resources have not always kept pace with innovation and expanded volumes. During the summer of 2015 in the province of Ontario, Canada, inadequate resources contributed to a capacity crisis, resulting in extended wait-lists for allo-SCT across the province. This situation presented unique ethical challenges, including the need for ongoing negotiations with health system partners and nimble process management to ensure timely delivery of care. This article reports on the process one organization used to determine how to equitably allocate scarce allo-SCT resources. With the ever-expanding landscape of new and emerging medical technologies, our experience has implications for the ethics of translating other increasingly expensive health technologies to clinical care.
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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 | 0.053 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.020 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".