A taxonomy of the factors contributing to the overtreatment of cancer patients at the end of life. What is the problem? Why does it happen? How can it be addressed?
Bibliographic record
Abstract
Many patients with cancer approaching the end of life (EOL) continue to receive treatments that are unlikely to provide meaningful clinical benefit, potentially causing more harm than good. This is called overtreatment at the EOL. Overtreatment harms patients by causing side-effects, increasing health care costs, delaying important discussions about and preparation for EOL care, and occasionally accelerating death. Overtreatment can also strain health care resources, reducing those available for palliative care services, and cause moral distress for clinicians and treatment teams. This article reviews the factors contributing to the overtreatment of patients with cancer at the EOL. It addresses the complex range of social, psychological, and cognitive factors affecting oncologists, patients, and patients' family members that contribute to this phenomenon. This intricate and complex dynamic complicates the task of reducing overtreatment. Addressing these driving factors requires a cooperative approach involving oncologists, oncology nurses, professional societies, public policy, and public education. We therefore discuss approaches and strategies to mitigate cultural and professional influences driving overtreatment, reduce the seduction of new technologies, improve clinician-patient communication regarding therapeutic options for patients approaching the EOL, and address cognitive biases that can contribute to overtreatment at the EOL.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".