Impact of Caregiver and Patient Age on Efficacy of Caregiver Interventions in Advanced Cancer: A Meta-Regression
Bibliographic record
Abstract
INTRODUCTION: It has been shown that interventions to support caregivers of patients with advanced cancer improve caregiver wellbeing, but the possible additional influence of patient and caregiver age is unknown. We measured the impact of patient and caregiver age on the efficacy of caregiver interventions. METHODS: We included all studies from a recent meta-analysis of randomized controlled trials of caregiver support interventions for adult caregivers of adults with advanced cancer that reported on outcomes of quality of life (QOL), anxiety, or depression. Random-effects meta regression was performed on each outcome for continuous variables of: 1) mean patient age, 2) mean caregiver age, and 3) mean age difference between mean age of patients and caregivers. Among study sets demonstrating statistically significant associations, we performed sensitivity analyses excluding influential outliers. RESULTS: Twenty-four studies reporting on 23 trials involving 3306 caregivers were included in this meta-regression. Interventions were associated with improved QOL, anxiety and depression. In studies with older mean patient age, interventions had a greater impact on QOL (P = 0.04). In studies with younger mean caregiver age, interventions had a greater impact on depression (P = 0.02). In studies with a larger patient-caregiver age difference, interventions had a greater impact on anxiety (P = 0.01), but this effect did not prevail on sensitivity analysis (P = 0.52). CONCLUSION: Age of patients with advanced cancer and their caregivers may influence the efficacy of caregiver support interventions, and should be taken into account when designing these interventions and interpreting their effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.080 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.091 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".