Palivizumab’s real-world effectiveness: a population-based study in Ontario, Canada, 1993–2017
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of two palivizumab programmes targeting high-risk infants, defined by prematurity, diagnosis of comorbidities and geography, and assess potential disparities by neighbourhood income. DESIGN: Controlled, interrupted time series. SETTING: Ontario, Canada. PATIENTS: We used linked health and demographic administrative databases to identify all children born in hospitals 1 January 1993 through 31 December 2016. Follow-up ended at the earliest of second birthday or 30 June 2017. INTERVENTION: Palivizumab-eligibility: child was born very preterm and ≤6 months old during respiratory syncytial virus (RSV) season; <24 months old with significant chronic lung or congenital heart disease; or ≤6 months, born preterm or residents of remote regions. MAIN OUTCOME: Severe RSV-related illness, defined as hospitalisation or death with a diagnosis of bronchiolitis, RSV pneumonia or RSV. RESULTS: 3 million births and 87 000 RSV-related events were identified. Over the study period, rates of severe RSV-related illness declined 65.4% among the highest risk group, eligible infants <6 months (230.6 to 79.8 admissions per 1000 child-years). Relative to changes among ineligible infants <6 months, rates dropped 10.4% (95% CI -18.6% to 39.4%) among eligible infants immediately following introduction of a national palivizumab programme in 1998. Initially, rates were considerably higher among infants from low-income neighbourhoods, but income-specific rates converged over time among eligible infants <6 months; such convergence was not seen among other children. CONCLUSIONS: Incidence of severe RSV-related illness declined over the study period. While we cannot attribute causality, the timing and magnitude of these declines suggest impact of palivizumab in reducing RSV burden and diminishing social inequities among palivizumab-eligible 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".