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 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 | 0.018 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".