Performing sit down medicine in a stand-up place: is it time for palliative care in the emergency department?
Bibliographic record
Abstract
Despite progress, the heart failure (HF) epidemic continues unabated worldwide.1 HF remains among the most common reasons for hospitalisations with the majority of these admissions occurring through the ED. Patients with advanced disease make up a disproportionately large share of hospitalised patients with HF and are particularly symptomatic with arguably the worst quality of life among chronically ill patients; hence, it is not a surprise that they end up in the ED frequently.1 It seems evident that palliative care, a specialty whose focus is symptom management, would provide significant benefit to those with advanced HF. Evidence suggests that palliative care specialists can improve quality of life and resource use among patients with HF. More recently, the Palliative Care in Heart Failure (PAL-HF) trial demonstrated improvements in physical, psychosocial and quality-of-life domains through a systemic approach to outpatient palliative care, involving a single nurse practitioner and palliative care specialist, in a high-risk HF population.2 Hence, it is no surprise that guidelines recommend palliative care for patients with advanced HF. Most studies suggest that it is vastly underused in this patient population.3 The intriguing manuscript by Lipinski et al further supports these previous studies. In this retrospective study of 500 patients with HF who presented to two different Canadian EDs, only 40% of patients who died within a year of ED presentation had palliative care …
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".