Risk factors that determine time to first RSV hospitalization in CARESS: The Canadian registry of palivizumab (2005-2011)
Bibliographic record
Abstract
Objective: Evaluate risk factors that determine time to first RSV hospitalization in children at high-risk of RSV infection who received prophylaxis. Design/Methods: A prospective, observational, registry of infants who received 1 dose of palivizumab during the 2005-2011 RSV seasons across 30 sites. Neonatal and demographic data were collected from the parent/caregiver at enrollment. Data related to respiratory infection events were collected monthly. Results: 10,452 infants were enrolled; average age 5.5±6.0 months. Infants were typically male (56.4%), Caucasian (71.3%), average gestational age (GA) 32.3±5.6 completed weeks. 7006 (67%) infants received palivizumab for prematurity (35 weeks GA) only, 836 (8 %) had chronic lung disease, 1048 (10%) had congenital heart disease and 1562 (15%) had underlying medical disorders (e.g. CNS disorders, airway anomalies and cystic fibrosis). Hospitalization rates for respiratory and RSV-related illness were 6.4% and 1.6%, respectively. Risk factors that predicted RSV hospitalization included: having siblings (HR=2.16, df=1, p=0.001), >5 people in household (HR=2.02, df=1, p Conclusions: Time to first RSV hospitalization after the first palivizumab dose are similar to those reported in the literature, with a natural history of RSV. The effect of multiple risk factors pose a cumulative increased risk for RSV hospitalization, similar to the Canadian and European risk scoring models for 33-35 weeks9 GA infants.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".