A Population-based Observational Study of Childhood Encephalitis in Children Admitted to Pediatric Intensive Care Units in England and Wales
Bibliographic record
Abstract
BACKGROUND: Encephalitis is a serious neurologic condition that can result in admission to intensive care. Yet, there are no studies on pediatric intensive care unit (PICU) admission rates and usage of intensive care resources by children with encephalitis in England and Wales. The objectives of this study were to (1) define the PICU incidence and mortality rates for childhood encephalitis, (2) describe the usage of intensive care resources by children with encephalitis admitted to PICU and (3) explore the associated cost from PICU encephalitis admissions. METHODS: Retrospective analysis of anonymized data for 1031 children (0-17 years) with encephalitis admitted (January 2003 to December 2013) to PICU in England and Wales. RESULTS: The PICU encephalitis incidence was 0.79/100,000 population/yr (95% confidence interval [CI]: 0.74-0.84), which gives an annual total of 214 bed days of intensive care occupancy for children admitted with encephalitis and an estimated annual PICU bed cost of £414,230 (interquartile range: 198,111-882,495) for this cohort. PICU encephalitis admissions increased during the study period (annual percentage change = 4.5%, 95% CI: 2.43%-6.50%, P ≤ 0.0001). In total, 808/1024 (78.9%) cases received invasive ventilation while 216/983 (22.0%) and 50/890 (5.6%) cases received vasoactive treatment and renal support, respectively. There were 87 deaths (8.4%), giving a PICU encephalitis mortality rate of 0.07/100,000 population (0-17 years)/yr (95% CI: 0.05-0.08). CONCLUSIONS: These data suggest that encephalitis places a significant burden to the healthcare service. More work is needed to improve outcomes for children with encephalitis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".