Characterising variation in composition and activation criteria of rapid response and cardiac arrest teams: a survey of Medicare participating hospitals in five American states
Bibliographic record
Abstract
OBJECTIVES: To characterise the variation in composition, leadership, and activation criteria of rapid response and cardiac arrest teams in five north-eastern states of the USA. DESIGN: Cross-sectional study consisting of a voluntary 46-question survey of acute care hospitals in north-eastern USA. SETTING: Acute care hospitals in New York, New Jersey, Rhode Island, Vermont, and Pennsylvania. PARTICIPANTS: Surveys were completed by any member of the rapid response team (RRT) with a working knowledge of team composition and function. Participants were all Medicare-participating acute care hospitals, including teaching and community hospitals as well as hospitals from rural, urban and suburban areas. RESULTS: Out of 378 hospitals, contacts were identified for 303, and 107 surveys were completed. All but two hospitals had an RRT, 70% of which changed members daily. The most common activation criteria were clinical concern (95%), single vital sign abnormalities (77%) and early warning score (59%). Eighty one per cent of hospitals had a dedicated cardiac arrest team.RRT composition varied widely, with respiratory therapists, critical care nurses, physicians and nurse managers being the most likely to attend (89%, 78%, 64% and 51%, respectively). Consistent presence of critical care physicians was uncommon and both cardiac arrest teams and teams were frequently led by trainee physicians, often without senior supervision. CONCLUSIONS: As the largest study to date in the USA, we have demonstrated wide heterogeneity, rapid team turnover and a lack of senior supervision of RRT and cardiac arrest teams. These factors likely contribute to the mixed results seen in studies of RRTs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".