Hospital Variation in Survival After Pediatric In-Hospital Cardiac Arrest
Bibliographic record
Abstract
BACKGROUND: Although survival after in-hospital cardiac arrest is likely to vary among hospitals caring for children,validated methods to risk-standardize pediatric survival rates across sites do not currently exist. METHODS AND RESULTS: From 2006 to 2010, within the American Heart Association's Get With the Guidelines-Resuscitation registry for in-hospital cardiac arrest, we identified 1551 cardiac arrests in children (<18 years). Using multivariable hierarchical logistic regression, we developed and validated a model to predict survival to hospital discharge and calculated risk-standardized rates of cardiac arrest survival for hospitals with a minimum of 10 pediatric cardiac arrest cases. A total of 13 patient-level predictors were identified: age, sex, cardiac arrest rhythm, location of arrest, mechanical ventilation, acute nonstroke neurological event, major trauma, hypotension, metabolic or electrolyte abnormalities, renal insufficiency, sepsis, illness category, and need for intravenous vasoactive agents prior to the arrest. The model had good discrimination (C-statistic of 0.71), confirmed by bootstrap validation (validation C-statistic of 0.69). Among 30 hospitals with ≥10 cardiac arrests, unadjusted hospital survival rates varied considerably (median, 37%; interquartile range, 24-42%; range, 0-61%). After risk-standardization, the range of hospital survival rates narrowed (median, 37%; interquartile range, 33-38%; range, 29-48%), but variation in survival persisted. CONCLUSIONS: Using a national registry, we developed and validated a model to predict survival after in-hospital cardiac arrest in children. After risk-standardization, significant variation in survival rates across hospitals remained. Leveraging these models, future studies can identify best practices at high-performing hospitals to improve survival outcomes for pediatric cardiac arrest. (
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".