Patient Preferences for Treatment Outcomes in Oncology with a Focus on the Older Patient—A Systematic Review
Bibliographic record
Abstract
For physicians, it is important to know which treatment outcomes are prioritized overall by older patients with cancer, since this will help them to tailor the amount of information and treatment recommendations. Older patients might prioritize other outcomes than younger patients. Our objective is to summarize which outcomes matter most to older patients with cancer. A systematic review was conducted, in which we searched Embase and Medline on 22 December 2020. Studies were eligible if they reported some form of prioritization of outcome categories relative to each other in patients with all types of cancer and if they included at least three outcome categories. Subsequently, for each study, the highest or second-highest outcome category was identified and presented in relation to the number of studies that included that outcome category. An adapted Newcastle-Ottawa Scale was used to assess the risk of bias. In total, 4374 patients were asked for their priorities in 28 studies that were included. Only six of these studies had a population with a median age above 70. Of all the studies, 79% identified quality of life as the highest or second-highest priority, followed by overall survival (67%), progression- and disease-free survival (56%), absence of severe or persistent treatment side effects (54%), and treatment response (50%). Absence of transient short-term side effects was prioritized in 16%. The studies were heterogeneous considering age, cancer type, and treatment settings. Overall, quality of life, overall survival, progression- and disease-free survival, and severe and persistent side effects of treatment are the outcomes that receive the highest priority on a group level when patients with cancer need to make trade-offs in oncologic treatment decisions.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".