A Systematic Review of Factors Influencing Older Adults’ Hypothetical Treatment Decisions
Bibliographic record
Abstract
Purpose:Cancer affects mostly older adults and although research has shown that a significant proportion of seniors do not receive treatment, little is known about the reasons why. Therefore, we conducted a systematic review of reasons why older adults accept or decline cancer treatments.Design:Systematic review of studies reporting on hypothetical cancer treatment scenarios in older patients published between inception of 10 databases and February 2013.Results:Of 17,343 abstracts reviewed, a total of 12 studies were included (sample size 21 to 511). The willingness to be treated varied by the benefits of treatment (ranging from never to always accepting the treatment), the particular side effects of treatment, and previous treatments received/previous treatment experiences (those who were treated previously were more likely to accept the same treatment). Results showed conflicting findings with regard to the impact of age, education (those with lower/higher age/education wanting more benefits before accepting), and family situation (no effect/those who were single were less likely to accept).Conclusion:Willingness among older adults to be treated was most influenced by the extent of benefits and side effects as well as prior treatment experiences. However, little is known about treatment preferences of the oldest old, those with multimorbidity, and preferences for newer agents.
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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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".