Respiratory Syncytial Virus Prophylaxis in Down Syndrome: A Prospective Cohort Study
Bibliographic record
Abstract
Background/Objective: Down syndrome (DS) children are at significant risk for respiratory tract (RTI) and respiratory syncytial virus (RSV) infection and hospitalization. We compared hospitalization rates for RTI in DS children aged < 2 years given palivizumab in the RSV season versus a previously published, similar untreated DS birth cohort. Methods: 532 prophylaxed DS children were from the Canadian palivizumab registry (CARESS) between 2005-2012. The untreated group comprised 233 DS children derived from a Dutch, nation-wide birth cohort from 2003-2005. Events during the RSV seasons were counted. Demographics and risk factors were compared using t-test or chi-square where appropriate. Poisson regression analysis was performed to compare incidence rate ratios [95% CI] for both RTI and confirmed RSV hospitalization between the 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 compa red to palivizumab recipients (incidence rate ratio 3.63 [95% CI: 1.52-8.67], p=0.004). The adjusted risk for hospitalization for all respiratory tract infection (147 events; 73 untreated, 74 treated) was similar (incidence rate ratio untreated versus palivizumab 1.11 [0.80 – 1.55], p=0.53). Conclusions: These results suggest that palivizumab is associated with a 3.6-fold reduction in the incidence rate ratio for RSV-related hospitalization in DS children aged <2 years. 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".