Respiratory hospitalizations and respiratory syncytial virus prophylaxis in special populations
Bibliographic record
Abstract
Palivizumab utilization, compliance, and outcomes were examined in infants with preexisting medical diseases within the Canadian Registry Database (CARESS) to aid in developing guidelines for potential "at-risk" infants in the future. Infants who received ≥1 dose of palivizumab during the 2006-2010 respiratory syncytial virus (RSV) seasons at 29 sites were recruited and utilization, compliance, and outcomes related to respiratory infection/illness (RI) events were collected monthly. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for premature infants ≤35 completed weeks gestational age (GA) who met standard approval criteria (group 1) compared to those with medical disorders (group 2) using Cox proportional hazards regression models with adjustment for potential confounding factors. Of 7,339 registry infants, 4,880 were in group 1 and 952 in group 2, which included those with Down syndrome (20.3%), upper airway anomalies (18.7%), pulmonary diseases (13.3%), and cystic fibrosis (12.3%). Group 2 were older at enrollment (10.2 ± 9.2 vs. 3.5 ± 3.1 months, p < 0.0005), had higher GA (35.9 ± 6.0 vs. 31.0 ± 5.4 weeks, p < 0.0005), and were less compliant with treatment intervals (69.4% vs. 72.6%, p = 0.048). A greater proportion of group 2 infants were hospitalized for RI (9.0% vs. 4.2%, p < 0.0005) and RSV (2.4% vs. 1.3%, p = 0.003) (unadjusted). Being in group 2 was associated with an increased risk of RI (HR = 2.0, 95%CI 1.5-2.5, p < 0.0005), but not RSV hospitalization (HR = 1.6, 95% CI 0.9-2.8, p = 0.106). In infants receiving palivizumab, those with underlying medical disorders, though not currently approved for prophylaxis, are at higher risk for RI events compared with preterm infants. However, risk of RSV hospitalizations is similar.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".