Palivizumab prophylaxis for respiratory syncytial virus (RSV) in infants with cystic fibrosis (CF) and respiratory illness hospitalizations
Bibliographic record
Abstract
Background: RSV in infants with CF has significant morbidities. Objectives: To evaluate effectiveness and safety of palivizumab RSV prophylaxis in infants with CF. Methods: Data on respiratory illness and RSV hospitalizations(RIH and RSVH) were collected in infants <2 years with CF in Alberta, Canada from 2000-2009. Outcomes in palivizumab-treated(PVZ) vs untreated(UPVZ) infants were compared with chi-square, Fisher9s exact, Mann-Whitney9s U or Student9s t-tests, and Poisson regression for factors influencing hospitalization. Results: 130 infants(43 PVZ;87 UPVZ) were included. PVZ group had more infants diagnosed by newborn screen(60.5% vs. 23.0%,χ2=17.7,df=1,p<0.005) and with failure to thrive(FTT) (55.8% vs. 37.9%,χ2=3.7,df=1,p=0.05) than UPVZ. 49 infants had a total of 82 RIH and 6 infants had 8 RSVH. PVZ treatment did not predict number of RIH(Incidence Rate Ratio [IRR]=1.1,95%CI=0.6-1.8,p=0.8), after adjustment for CF diagnosis method and FTT, while FTT itself was a significant predictor(IRR=1.7,95%CI=1.1-2.7,p=0.03). RSVH was not predicted by PVZ treatment(IRR=0.3,95%CI=0.04-2.84,p=0.3), method of CF diagnosis(IRR=0.4,95%CI=0.04-3.32,p=0.4) nor FTT(IRR=1.5,95%CI=0.4-6.0,p=0.5). For RSVH, there were no differences between groups in days of intensive care(mean 5.0±5.7,n=2), O2 use(mean 6.5±0.7,n=2) or total length of stay(mean 34.5±36.1,n=6,U=0.00,p=0.3). There were no serious adverse events related to PVZ. Conclusions: There were no differences in RSVH and RIH or morbidities between groups. Though sample size is one of the largest reported, power may be insufficient to detect differences. Further larger prospective studies are needed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".