An Assessment of Emergency Department-Based Interventions for Patients with Advanced or End-Stage Illness: A Systematic Review
Bibliographic record
Abstract
Background and Objective: With the increase of visits among patients with end-of-life needs, palliative care (PC) interventions delivered in the emergency department (ED) have become increasingly important. The objective of this systematic review was to examine the effectiveness of ED-based PC interventions. Methods: A comprehensive search of the literature was conducted to identify any comparative studies assessing the effectiveness of ED-based PC interventions. Two independent reviewers completed study selection, quality assessment, and data extraction. Relative risks (RR) with 95% confidence intervals (CIs) were calculated using a random effects model and heterogeneity (I2) was reported. Results: A total of 18 unique studies were included. Two studies reported no difference in return visits to the ED (RR = 1.31; 95% CI: 0.73–2.35; I2 = 47%). Two randomized trials reported no difference in mortality (RR = 0.89; 95% CI: 0.71–1.13; I2 = 0%), while one cohort study reported an increased mortality among patients referred to PC in the ED (RR = 1.89; 95% CI: 1.58–2.27). Overall, six out of eight studies reported a decrease in hospital length of stay (LOS) among patients undergoing an ED-based PC intervention compared with usual care. Conclusions: While there is compelling evidence to suggest that ED-based PC interventions can reduce hospital LOS, the evidence for the impact of these interventions on other outcomes is less robust. More high-quality comparative studies are needed to better understand the overall impact of ED-based PC interventions on improving patient outcomes as well as improving throughput and other quality of service-related 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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".