Burden of Healthcare-Associated Viral Respiratory Infections in Children’s Hospitals
Bibliographic record
Abstract
OBJECTIVE: Although healthcare-associated (HA) viral respiratory infections (VRIs) are common in pediatrics, no benchmark for comparison exists. We aimed to determine, compare, and assess determinants of unit-specific HA-VRI incidence rates in 2 children's hospitals. METHODS: This study was a retrospective comparison of prospective cohorts. The Montreal Children's Hospital and the Cohen Children's Medical Center of New York perform prospective surveillance for HA-VRI using standardized definitions that require the presence of symptoms compatible with VRI and virus detection. Cases detected between April 1, 2010, and March 31, 2013, were identified using surveillance databases. Annual incidence rates were calculated, and a generalized estimating equation model was used to assess determinants of HA-VRI rates. RESULTS: The overall HA-VRI rate during the 3-year study period was significantly higher at Montreal Children's Hospital than that at Cohen Children's Medical Center of New York (1.91 vs 0.80 per 1000 patient-days, respectively; P < .0001). Overall, the HA-VRI incidence rate was lowest in the neonatal intensive care unit. Rates in the pediatric intensive care, oncology, and medical/surgical units were similar. The most common etiology of HA-VRI at both institutions was rhinovirus (49% of cases), followed by parainfluenza virus and respiratory syncytial virus. Hospitals with less than 50% single rooms had HA-VRI rates 1.33 (95% confidence interval, 1.29-1.37) times higher than hospitals with more than 50% single rooms for a given unit type. CONCLUSIONS: HA-VRI rates were substantial but different among 2 children's hospitals. Future studies should examine the effect of HA-VRI and evaluate best practices for preventing such infections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".