Pediatric Emergency Department Sepsis Screening Tool Accuracy During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Automated sepsis alerts in pediatric emergency departments (EDs) can identify patients at risk for sepsis, allowing for earlier intervention with appropriate therapies. The impact of the COVID-19 pandemic on the performance of pediatric sepsis alerts is unknown. METHODS: We performed a retrospective cohort study of 59 335 ED visits before the pandemic and 51 990 ED visits during the pandemic in an ED with an automated sepsis alert based on systemic inflammatory response syndrome criteria. The sensitivity, specificity, negative predictive value, and positive predictive value of the sepsis algorithm were compared between the prepandemic and pandemic phases and between COVID-19-negative and COVID-19-positive patients during the pandemic phase. RESULTS: The proportion of ED visits triggering a sepsis alert was 7.0% (n = 4180) before and 6.1% (n = 3199) during the pandemic. The number of sepsis alerts triggered per diagnosed case of hypotensive septic shock was 24 in both periods. There was no difference in the sensitivity (74.1% vs 72.5%), specificity (93.2% vs 94.0%), positive predictive value (4.1% vs 4.1%), or negative predictive value (99.9% vs 99.9%) of the sepsis alerts between these periods. The alerts had a lower sensitivity (60% vs 73.3%) and specificity (87.3% vs 94.2%) for COVID-19-positive versus COVID-19-negative patients. CONCLUSIONS: The sepsis alert algorithm evaluated in this study did not result in excess notifications and maintained adequate performance during the COVID-19 pandemic in the pediatric ED setting.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".