A systematic review of factors influencing older adults’ decision to accept or decline cancer treatment
Bibliographic record
Abstract
BACKGROUND: Cancer is a disease that affects mostly older adults. Older adults often have other chronic health conditions in addition to cancer and may have different health priorities, both of which can impact cancer treatment decision-making. However, no systematic review of factors that influence an older cancer patient's decision to accept or decline cancer treatment has been conducted. MATERIALS AND METHODS: Systematic review of the literature published between inception of the databases and February 2013. Dutch, English, French or German articles reporting on qualitative studies, cross-sectional, longitudinal observational or intervention studies describing factors why older adults accepted or declined cancer treatment examining actual treatment decisions were included. Ten databases were used. Two independent reviewers reviewed manuscripts and performed data abstraction using a standardized form and the quality of studies was assessed with the Mixed Methods Appraisal Tool. RESULTS: Of 17,343 abstracts reviewed, a total of 38 studies were included. The majority focused on breast and prostate cancer treatment decisions and most studies used a qualitative design. Important factors for accepting treatment were convenience and success rate of treatment, seeing necessity of treatment, trust in the physician and following the physician's recommendation. Factors important for declining cancer treatment included concerns about the discomfort of the treatments, fear of side effects and transportation difficulties. CONCLUSION: Although the reasons why older adults with cancer accepted or declined treatment varied considerably, the most consistent determinant was physician recommendation. Further studies using large, representative samples and exploring decision-making incorporating health literacy and comorbidity are needed.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".