Bioethics of over- and undertreatment in older adults with cancer.
Bibliographic record
Abstract
9046 Background: Over-/undertreatment are pervasive in older adults with cancer, despite oncologists prescribing with best intentions. What “ought” to be prescribed with limited evidence creates challenges in adhering to the principles of bioethics: beneficence, nonmaleficence, justice, and respect for autonomy. Our objective for this study was to elucidate whether and how tensions among these ethical principles can contribute to over-/undertreatment in older patients. Methods: We designed a modified Delphi study, convening a panel of 13 experts in biomedical ethics (5 male, 8 female; 4 MD, 4 PhD, 2 MD/MA, 1 MD/PhD, 1 JD/MDiv, 1 DNP) from U.S. and Canadian institutions for three iterative rounds of data collection. In the first round—an electronic questionnaire—we presented definitions of overtreatment and undertreatment in older adults with cancer (DuMontier, J Clin Oncol, 2020) and asked questions delineating which ethical principles related to each definition, followed by questions regarding how over-/undertreatment might occur from conflicts among different ethical principles. Consensus for each question was defined as ≥75% of experts answering “agree” or “strongly agree”. The second round consisted of a virtual synchronous focus group of 9 of the panel experts led by a qualitative researcher to review round one results and discuss questions that did not reach consensus, followed by a second questionnaire including these questions. Results: After the first round, experts reached consensus that bioethical principles applied to over-/undertreatment in older adults with cancer. Specifically, 92% felt that overtreatment can occur when oncologists overemphasize beneficence that values the potential benefit of cancer treatments, while underemphasizing non-maleficence with respect to treatment adverse effects. Moreover, 77% felt that overtreatment can also occur when oncologists prioritize patient autonomy (preference to be treated) over non-maleficence (oncologists' concerns that treatment harms outweigh benefits). 84% felt that undertreatment can occur due to a lack of justice in equitable consideration of cancer treatments that could provide similar benefits in older adults as they would in younger adults. Moreover, 77% felt that undertreatment can occur when oncologists underemphasize patient autonomy, failing to consider patient preferences regarding which benefits to pursue and risks to take. Data collection for the second questionnaire and qualitative analysis of the focus group are underway. Conclusions: Our findings suggest that tension in ethical principles can lead to over- and undertreatment in older adults with cancer. The “right” treatment in older patients in the context of limited evidence is not simply one that aims to aggressively target their cancer, but that balances both benefits and harms in light of the whole patient and their preferences, while not restricting therapies based on age alone.
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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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".