Reduction in Hospital Mortality Over Time in a Hospital Without a Pediatric Medical Emergency Team
Bibliographic record
Abstract
OBJECTIVE: To determine whether hospital mortality has decreased over time in a hospital that has not introduced a pediatric medical emergency team (PMET). DESIGN: Retrospective observational study. SETTING: Quaternary children's hospital. PARTICIPANTS: All pediatric inpatient separations (defined as any discharge, including death) during 10 fiscal years. MAIN OUTCOME MEASURES: We searched our hospital administrative database to determine the number of pediatric inpatient separations and deaths, and we searched the hospital switchboard and pediatric intensive care databases to determine ward code and cardiopulmonary arrest rates. Relative risks (RRs) with 95% confidence intervals (CIs) and logistic regression compared results over time. RESULTS: During the periods of the 2 PMET studies showing a reduction in hospital mortality, we found a decrease in hospital mortality: for 1999-2002 vs 2002-2006, 212 deaths among 14 161 patients (1.50%) vs 219 of 26 767 (0.82%), RR, 0.55 (95% CI, 0.44-0.69); for 2000-2005 vs 2005-2007, 300 deaths among 29 497 patients (1.02%) vs 98 of 14 005 (0.70%), RR, 0.69 (95% CI, 0.55-0.86). During the periods of the 3 PMET studies showing no change in or not examining hospital mortality, we found no significant change in hospital mortality. The annual odds ratio for survival was 1.13 (95% CI, 1.09-1.16). There were no changes in ward code and cardiopulmonary arrest rates over time. CONCLUSIONS: We found a reduction in hospital mortality over time in a children's hospital without a PMET. This demonstrates the limitation of before-and-after study designs, and we hypothesize that multiple co-interventions account for the decrease in mortality. Whether a PMET could have reduced mortality further is unknown.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".