Video decision aids to assist with advance care planning: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Advance care planning (ACP) can result in end-of-life care that is more congruent with patients' values and preferences. There is increasing interest in video decision aids to assist with ACP. The objective of this study was to evaluate the impact of video decision aids on patients' preferences regarding life-sustaining treatments (primary outcome). DESIGN: Systematic review and meta-analysis of randomised controlled trials. DATA SOURCES: MEDLINE, EMBASE, PsycInfo, CINAHL, AMED and CENTRAL, between 1980 and February 2014, and correspondence with authors. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Randomised controlled trials of adult patients that compared a video decision aid to a non-video-based intervention to assist with choices about use of life-sustaining treatments and reported at least one ACP-related outcome. DATA EXTRACTION: Reviewers worked independently and in pairs to screen potentially eligible articles, and to extract data regarding risk of bias, population, intervention, comparator and outcomes. Reviewers assessed quality of evidence (confidence in effect estimates) for each outcome using the Grading of Recommendations Assessment, Development and Evaluation framework. RESULTS: 10 trials enrolling 2220 patients were included. Low-quality evidence suggests that patients who use a video decision aid are less likely to indicate a preference for cardiopulmonary resuscitation (pooled risk ratio, 0.50 (95% CI 0.27 to 0.95); I(2)=65%). Moderate-quality evidence suggests that video decision aids result in greater knowledge related to ACP (standardised mean difference, 0.58 (95% CI 0.38 to 0.77); I(2)=0%). No study reported on the congruence of end-of-life treatments with patients' wishes. No study evaluated the effect of video decision aids when integrated into clinical care. CONCLUSIONS: Video decision aids may improve some ACP-related outcomes. Before recommending their use in clinical practice, more evidence is needed to confirm these findings and to evaluate the impact of video decision aids when integrated into patient care.
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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 | 0.024 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".