Patient and Family Caregiver Perspectives on Therapy De‐Escalation in Cancer: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Cancer therapy de-escalation aims to reduce treatment intensity, minimizing the burden of short- and long-term toxicities on patients and family caregivers while maintaining current survival rates. While this approach holds potential benefits, it comes at a risk of worse patient health outcomes or treatment failure. An understanding of patient and family caregiver perspectives regarding cancer therapy de-escalation is required to design successful patient-and-caregiver-informed clinical trials, and optimally provide related patient-centered care. AIM: To identify and synthesize the literature about patient and family caregiver perspectives of cancer therapy de-escalation to guide clinical care, research, decision-support resources, and education. METHODS: Following the Joanna Briggs Institute methodology, a systematic literature search was conducted in MEDLINE, EMBASE, PsycINFO, and CINAHL. We included quantitative, qualitative, and mixed-methods studies involving patients of all ages and cancer diagnoses and their family caregivers that focused on perceptions of cancer therapy de-escalation. Extracted data were organized according to the Framework for De-implementation in Cancer Care Delivery. Study quality was appraised. RESULTS: Twenty studies were included. De-escalation perspectives varied between patients and family caregivers, with factors including clinician trust and desire to improve quality of life noted as influential in de-escalation decisions. The decision-making process could be better supported through the provision of timely patient and family caregiver information and clinician communication training. CONCLUSION: Cancer therapy de-escalation decisions are complex and multifactorial. Future research exploring which factors influence patient and family decision-making may offer insight into the design of optimal informational and supportive interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".