Evaluating a New Rapid Response Team
Bibliographic record
Abstract
Evidence is needed to validate rapid response teams (RRTs), including those led by nurse practitioners (NPs). A descriptive-comparative mixed-methods study was undertaken to evaluate a newly implemented NP-led RRT at 2 Canadian hospitals. On the basis of data gathered on 255 patients who received an RRT call compared with the patient data for the previous year, no significant differences in the number of cardiorespiratory arrests, unplanned intensive care unit admissions, and hospital mortality were found. In addition, no significant differences in patient outcomes were identified between the NP-led and intensivist physician-led RRT calls. A paper survey revealed that ward nurses had confidence in the knowledge and skills of the NP-led RRT and believed that patient outcomes were improved as a result of their RRT call. These findings indicate that NP-led RRTs are a safe and effective alternative to intensivist-led teams, but more research is needed to demonstrate that RRTs improve hospital care quality and patient 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 | 0.159 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".