Risk factors for severe RSV-induced lower respiratory tract infection over four consecutive epidemics
Bibliographic record
Abstract
UNLABELLED: Variability in severity among different respiratory syncytial virus (RSV) seasons may influence hospital admission rates for RSV-induced lower respiratory tract infection (LRTI) in young children. The aim of the present study was to identify through logistic regression analysis, risk factors associated with higher likelihood to acquire RSV-induced LRTI, in children with symptoms severe enough to lead to hospital admission. Over four consecutive RSV seasons (2000-2004), records from children <4 years of age admitted for RSV-induced LRTI ("cases") were compared with those from children with LRTI not due to RSV and not requiring hospitalization ("controls"). 145 "case-patients" and 295 "control-patients" were evaluated. Independent from the severity of the four epidemic seasons, seven predictors for hospitalization for RSV infection were found in the bivariate analysis: number of children in the family, chronological age at the onset of RSV season, birth weight and gestational age, birth order, daycare attendance, previous RSV infections. In the logistic regression analysis, only three predictors were detected: chronological age at the beginning of RSV season [aOR =8.46; 95% CI:3.09-23.18]; birth weight category [aOR =7.70; 95% CI:1.29-45.91]; birth order (aOR =1.92; 95% CI:1.21-3.06). CONCLUSIONS: Independent from the RSV seasonality, specific host/environmental factors can be used to identify children at greatest risk for hospitalization for RSV infection.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".