Respiratory Syncytial Virus Prophylaxis in Down Syndrome: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Children with Down syndrome (DS) are at significant risk for respiratory syncytial virus (RSV) infection and related hospitalization. We compared hospitalization rates due to respiratory tract infection in children with DS aged <2 years who prospectively received palivizumab during the RSV season with a previously published, similar untreated DS birth cohort. METHODS: A total of 532 children with DS who prospectively received palivizumab were assembled from the prospective Canadian RSV Evaluation Study of Palivizumab registry between 2005 and 2012. The untreated group included 233 children with DS derived from a nationwide Dutch birth cohort from 2003 to 2005. Events during the RSV seasons were counted. Poisson regression analysis was performed to compare incidence rate ratios (95% confidence intervals [CIs]) between groups while controlling for observation length and known risk factors for severe RSV infection. RESULTS: In total, 31 (23 untreated, 8 treated) RSV-related hospitalizations were documented. The adjusted risk of RSV-related hospitalizations was higher in untreated subjects than in palivizumab recipients (incidence rate ratio 3.63; 95% CI, 1.52-8.67). The adjusted risk of hospitalization for all respiratory tract infection (147 events; 73 untreated, 74 treated) was similar (incidence rate ratio untreated versus palivizumab 1.11; 95% CI, 0.80-1.55). CONCLUSIONS: These results suggest that palivizumab is associated with a 3.6-fold reduction in the incidence rate ratio for RSV-related hospitalization in children with DS during the first 2 years of life. A randomized trial is needed to determine the efficacy of RSV immunoprophylaxis in this specific high-risk patient population.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".