Goals of care discussions in hospitalised patients: systematic review of randomised trials
Bibliographic record
Abstract
IMPORTANCE: Structured communication tools and decision aids are recommended to facilitate goals of care discussions (GOCDs) in hospitalised patients, but their impact remains unclear. OBJECTIVE: To determine the effectiveness of communication tools and decision aids on GOCDs between healthcare providers, patients and substitute decision makers. DATA SOURCES: PubMed, Web of Science, CINAHL and ClinicalTrials.gov were searched from January 2015 to July 2025, with backward citation tracking to identify additional trials. STUDY SELECTION: Randomised trials of communication tools or decision aids for hospitalised adults or substitute decision makers were eligible, regardless of primary outcomes. DATA EXTRACTION AND SYNTHESIS: Trial characteristics, primary outcomes and methodological quality were extracted and assessed using the Core Grading of Recommendations, Assessment, Development and Evaluation framework. Random-effects meta-analyses were conducted when appropriate. RESULTS: 22 trials were included. Communication tools increased GOCD documentation (OR 1.46, 95% CI 1.04 to 2.05). Effects on resuscitation preferences were inconsistent (OR 1.84, 95% CI 0.95 to 3.60). Mixed effects were observed for concordant care, psychosocial outcomes and healthcare utilisation. Evidence certainty was moderate for most outcomes; several small pilot trials were rated low due to bias, imprecision or heterogeneity. Interventions were most effective when combining structured patient-facing or clinician-facing prompts such as videos, guides or electronic health record alerts. CONCLUSIONS AND RELEVANCE: Communication tools and decision aids improve GOCD documentation in hospitalised patients but show uncertain effects on other patient-centred outcomes or healthcare utilisation. Interprofessional training and system-level support may enhance impact, and future large trials should evaluate the effectiveness of facilitated GOCDs on important patient and health system outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".