A Cross-sectional Survey of Levels of Care and Response Mechanisms for Evolving Critical Illness in Hospitalized Children
Bibliographic record
Abstract
OBJECTIVES: Recognition and treatment of evolving critical illness is a fundamental element of hospital care. Hospital systems should triage patients to receive appropriate levels of care. We describe here the levels of care, the frequency of near or actual cardiopulmonary arrest (code-blue events), identification mechanisms, and responses to evolving critical illness in hospitalized children. METHODS: A cross-sectional telephone survey of Canadian and American hospitals with > or = 50 pediatric acute care beds or > or = 2 pediatric wards was performed. Regression analysis identified factors associated with the frequency of code-blue events after adjustment for hospital volume. RESULTS: Responses from 388 (84%) hospitals identified the 181 eligible pediatric hospitals included in this survey. All had a PICU, 99 (55%) had high-dependency units, 101 (56%) had extracorporeal membrane oxygenation therapy, and 69 (38%) used extracorporeal membrane oxygenation therapy for refractory cardiopulmonary arrest. All of the hospitals had immediate-response teams. They were activated 4676 times in the previous 12 months. Twenty-four percent of hospitals had activation criteria for immediate-response teams. Urgent-response teams to treat children who were clinically deteriorating but not at immediate risk of cardiopulmonary arrest were available in 136 (75%) hospitals; 29 (17%) had formal medical emergency teams, and 92 (51%) consulted the PICU. Code-blue events were more common in hospitals with extracorporeal membrane oxygenation therapy, cardiopulmonary bypass, and larger PICU size. CONCLUSIONS: Currently, the organization of Canadian and American pediatric hospitals includes dedicated areas to match patient acuity and additional personnel to stabilize and facilitate transfer. The functioning of these systems of care results in calls for immediate medical assistance for ward patients approximately 5000 times annually.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".