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 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 | 0.054 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.014 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".