Presumed Systemic Inflammatory Response Syndrome in the Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the incidence and outcomes of patients presenting with systemic inflammatory response syndrome (SIRS) in the pediatric emergency department (PED). METHODS: This was a descriptive, retrospective cohort study of all patients from birth to 18 years presenting to the PED of a single center on 16 days distributed over 1 year. The presence of presumed SIRS (pSIRS, defined as noncore temperature measurement and cell count when clinically indicated) and sepsis was determined for all study patients. Patients were followed up for 1 week. RESULTS: The incidence of pSIRS was 15.3% (216/1416). Suspected or proven infection was present in 37.1% (n = 525) of the study population and 76.4% (n = 165) with pSIRS, with no cases of severe sepsis or septic shock. Sensitivity and specificity of pSIRS for predicting infection were 31.4% (95% confidence interval [CI], 27.5%-35.6%) and 94.3% (95% CI, 92.5%-95.7%), respectively. Although patients with pSIRS had a relative risk of 2.4 (95% CI, 1.6-3.5; P < 0.0001) for admission, 74% were discharged home with no subsequent PED visits. Of defined sepsis cases, 75% were discharged home without return. CONCLUSIONS: Presumed SIRS and sepsis are relatively common in the PED. Use of pSIRS to screen for sepsis risks missing infection, whereas using pSIRS in the current sepsis definition results in overinclusion of nonsevere illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 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".