Shared Decision Making to Support the Provision of Palliative and End‐of‐Life Care in the Emergency Department: A Consensus Statement and Research Agenda
Bibliographic record
Abstract
BACKGROUND: Little is known about the optimal use of shared decision making (SDM) to guide palliative and end-of-life decisions in the emergency department (ED). OBJECTIVE: The objective was to convene a working group to develop a set of research questions that, when answered, will substantially advance the ability of clinicians to use SDM to guide palliative and end-of-life care decisions in the ED. METHODS: Participants were identified based on expertise in emergency, palliative, or geriatrics care; policy or patient-advocacy; and spanned physician, nursing, social work, legal, and patient perspectives. Input from the group was elicited using a time-staggered Delphi process including three teleconferences, an open platform for asynchronous input, and an in-person meeting to obtain a final round of input from all members and to identify and resolve or describe areas of disagreement. CONCLUSION: Key research questions identified by the group related to which ED patients are likely to benefit from palliative care (PC), what interventions can most effectively promote PC in the ED, what outcomes are most appropriate to assess the impact of these interventions, what is the potential for initiating advance care planning in the ED to help patients define long-term goals of care, and what policies influence palliative and end-of-life care decision making in the ED. Answers to these questions have the potential to substantially improve the quality of care for ED patients with advanced illness.
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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 | 0.317 | 0.232 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".