Development of a Pragmatic Measure for Evaluating and Optimizing Rapid Response Systems
Bibliographic record
Abstract
OBJECTIVES: Standard metrics for evaluating rapid response systems (RRSs) include cardiac and respiratory arrest rates. These events are rare in children; therefore, years of data are needed to evaluate the impact of RRSs with sufficient statistical power. We aimed to develop a valid, pragmatic measure for evaluating and optimizing RRSs over shorter periods of time. METHODS: We reviewed 724 medical emergency team and 56 code-blue team activations in a children's hospital between February 2010 and February 2011. We defined events resulting in ICU transfer and noninvasive ventilation, intubation, or vasopressor infusion within 12 hours as "critical deterioration." By using in-hospital mortality as the gold standard, we evaluated the test characteristics and validity of this proximate outcome metric compared with a national benchmark for cardiac and respiratory arrest rates, the Child Health Corporation of America Codes Outside the ICU Whole System Measure. RESULTS: Critical deterioration (1.52 per 1000 non-ICU patient-days) was more than eightfold more common than the Child Health Corporation of America measure of cardiac and respiratory arrests (0.18 per 1000 non-ICU patient-days) and was associated with >13-fold increased risk of in-hospital death. The critical deterioration metric demonstrated both criterion and construct validity. CONCLUSIONS: The critical deterioration rate is a valid, pragmatic proximate outcome associated with in-hospital mortality. It has great potential for complementing existing patient safety measures for evaluating RRS performance.
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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 | 0.082 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".