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?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it