Risk of Repeated Admissions for Respiratory Syncytial Virus in a Cohort of >10 000 Hospitalized Children
Bibliographic record
Abstract
BACKGROUND: The objective was to describe respiratory syncytial virus (RSV) hospitalizations in Alberta, Canada over a 13-year period with an emphasis on the incidence and risk factors for repeat hospitalizations attributable to new RSV infections. METHODS: This was a retrospective database analysis. The Alberta Health Services Discharge Abstract Database was searched for patients <5 years of age admitted to any hospital with a primary diagnosis of RSV from July 1, 2004 through June 30, 2017. Clinical characteristics were compared for children with repeat RSV admission during the same RSV season (but >30 days apart so presumably due to separate infections) compared with all other children with RSV admissions. RESULTS: During the study period, 10 212 children had 10 967 RSV admissions. The RSV hospitalization rate was 1.6%. A total of 666 children (6.5%) were readmitted for RSV at least once during the study period, of whom 433 (4.2%) were readmitted within 30 days of the initial hospital discharge. There were 36 children (0.35%) with 2 RSV admissions >30 days apart during the same RSV season. When compared to all other children with RSV admissions, they were more likely to have congenital heart disease or to have been diagnosed with RSV pneumonia (vs bronchiolitis or upper respiratory tract infection) during their initial hospitalization. CONCLUSIONS: The RSV hospitalization rate in children <5 years of age was 1.6%. Repeat RSV infections requiring readmission during the same RSV season occurred following only 0.35% of RSV hospitalizations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".