Challenges in clarifying goals of care in patients with advanced heart failure
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with advanced heart failure require communication about goals of care, yet many challenges exist, leaving it suboptimal. High mortality rates and advances in the use of life-sustaining technology further complicate communication and underscore the urgency to understand and address these challenges. This review highlights current research with a view to informing future research and practice to improve goals of care communication. RECENT FINDINGS: Clinicians view patient and family barriers as more impactful than clinician and system factors in impeding goals of care discussions. Knowledge gaps about life-sustaining technology challenge timely goals of care discussions. Complex, nurse-led interventions that activate patient, clinician and care systems and video-decision aids about life-sustaining technology may reduce barriers and facilitate goals of care communication. SUMMARY: Clinicians require relational skills in facilitating goals of care communication with diverse patients and families with heart failure knowledge gaps, who may be experiencing stress and discord. Future research should explore the dynamics of goals of care communication in real-time from patient, family and clinician perspectives, to inform development of upstream and complex interventions that optimize communication. Further testing of interventions is needed in and across community and hospital settings.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".