The Effect of Rapid Response Teams on End‐of‐Life Care: A Retrospective Chart Review
Bibliographic record
Abstract
BACKGROUND: A subset of critically ill patients have end-of-life (EOL) goals that are unclear. Rapid response teams (RRTs) may aid in the identification of these patients and the delivery of their EOL care. OBJECTIVES: To characterize the impact of RRT discussion on EOL care, and to examine how a preprinted order (PPO) set for EOL care influenced EOL discussions and outcomes. METHODS: A single-centre retrospective chart review of all RRT calls (January 2009 to December 2010) was performed. The effect of RRT EOL discussions and the effect of a hospital-wide PPO set on EOL care was examined. Charts were from the Ontario Ministry of Health and Long-Term Care Critical Care Information Systemic database, and were interrogated by two reviewers. RESULTS: In patients whose EOL status changed following RRT EOL discussion, there were fewer intensive care unit (ICU) transfers (8.4% versus 17%; P<0.001), decreased ICU length of stay (5.8 days versus 20 days; P=0.08), increased palliative care consultations (34% versus 5.3%; P<0.001) and an increased proportion who died within 24 h of consultation (25% versus 8.3%; P<0.001). More patients experienced a change in EOL status following the introduction of an EOL PPO, from 20% (before) to 31% (after) (P<0.05). CONCLUSIONS: A change in EOL status following RRT-led EOL discussion was associated with reduced ICU transfers and enhanced access to palliative care services. Further study is required to identify and deconstruct barriers impairing timely and appropriate EOL discussions.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".