Paediatric cardiac rapid response systems: a survey of multicentre practices
Bibliographic record
Abstract
INTRODUCTION: While the efficacy and guidelines for implementation of rapid response systems are well established, limited information exists about rapid response paradigms for paediatric cardiac patients despite their unique pathophysiology. METHODS: With endorsement from the Paediatric Cardiac Intensive Care Society, we designed and implemented a web-based survey of paediatric cardiac and multidisciplinary ICU medical directors in the United States of America and Canada to better understand paediatric cardiac rapid response practices. RESULTS: Sixty-five (52%) of 125 centres responded. Seventy-one per cent of centres had ∼300 non-ICU beds and 71% had dedicated cardiac ICUs. To respond to cardiac patients, dedicated cardiac rapid response teams were utilised in 29% of all centres (39% and 5% in centres with and without dedicated cardiac ICUs, respectively) [p = 0.006]. Early warning scores were utilised in 62% of centres. Only 31% reported that rapid response teams received specialised training. Transfers to ICU were higher for cardiac (73%) compared to generalised rapid response events (54%). The monitoring and reassessment of patients not transferred to ICU after the rapid response was variable. Cardiac and respiratory arrests outside the ICU were infrequent. Only 29% of centres formally appraise critical deterioration events (need for ventilation and/or inotropes post-rapid response) and 34% perform post-event debriefs. CONCLUSION: Paediatric cardiac rapid response practices are variable and dedicated paediatric cardiac rapid response systems are infrequent in the United States of America and Canada. Opportunity exists to delineate best practices for paediatric cardiac rapid response and standardise practices for activation, training, patient monitoring post-rapid response events, and outcomes evaluation.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".