Factors associated with severe respiratory syncytial virus disease in hospitalised children: a retrospective analysis
Bibliographic record
Abstract
BACKGROUND: Early recognition of children at risk of severe respiratory syncytial virus (RSV) lower respiratory tract infection is important as it informs management decisions. We aimed to evaluate factors associated with severe disease among young children hospitalised with RSV infection. METHODS: We conducted a retrospective cohort study of all children <2 years of age hospitalised for RSV lower respiratory tract infection at a single tertiary paediatric hospital over three RSV seasons (January 2017-December 2019). We classified children as having 'moderate' or 'severe' disease based on the level of respiratory intervention and used univariable and multivariable regression models to determine factors associated with severe disease. RESULTS: Of 970 hospitalised children, 386 (40%) were classified as having 'severe' and 584 (60%) as having 'moderate' RSV disease. On multivariable analyses, age <2 months (OR: 2.3, 95% CI 1.6 to 3.3, p<0.0001), prematurity (OR: 1.6, 95% CI 1.1 to 2.4, p=0.02) and RSV-parainfluenza virus type 3 (PIV3) codetection (OR: 2.6, 95% CI 1.05 to 6.5, p=0.04) were independently associated with severe disease. CONCLUSION: Younger age, prematurity and PIV3 codetection were associated with severe RSV disease in children <2 years of age hospitalised with RSV infection. The association between PIV3 and severe RSV disease is a novel finding and warrants further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".