Systematic review of interventions to facilitate advance care planning (ACP) in cancer patients.
Bibliographic record
Abstract
21 Background: ACP refers to the process of consideration, documentation and communication of preferences for future care. ACP is crucial for patients (pts) with advanced cancer as it can guide substitute decision makers (SDM) and health care providers (HCP) to align care with preferences, thus improving quality of end-of-life care. Methods: We performed a systematic review of MEDLINE, EMBASE, CINAHL, PsycINFO and Cochrane (Systematic Review and Clinical Trial) databases (1995 to 2015) to identify interventions that facilitate ACP for cancer pts (documentation or discussion of advance directives, SDM or code status). We extracted data on study design, setting, subject numbers, interventions and outcomes. Study quality was assessed using a modified Downs and Black checklist. Results: Of the 64,196 unique citations identified, 10 studies met inclusion criteria for testing interventions using a pre-post or controlled trial design (median sample size 134, range 48-9105). Interventions were categorized based on target audience: health system (n = 4), pts and caregivers (n = 3) or HCP (n = 3). Types of interventions included: introduction of ACP facilitators (n = 4), reminders or prompts (n = 2), and HCP training, videos, website or pt screening (n = 1 each). Heterogeneity in study design, outcome measures and small sample sizes limited study quality and precluded meta-analysis. Prompts such as medical record or email reminders were most consistently associated with improved ACP documentation. System changes incorporating the use of ACP facilitators also improved ACP documentation in 3 out of 4 studies. Interactive HCP training significantly improved confidence to initiate ACP discussions which has been identified as a barrier to timely ACP. Pilot trials showed no significant increase in ACP with educational videos/websites directed at pts. Passive HCP education and one-off reminders were also ineffective. Conclusions: The complexity of ACP is reflected in the multitude of interventions that have been evaluated but none are ready for wide-scale adoption. Further studies of interventions such as prompts and ACP facilitators are needed to inform the best approach to improve ACP uptake in cancer pts.
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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 | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".