Optimizing risk factors to guide COST‐effective use of palivizumab in KOREAN infants
Bibliographic record
Abstract
BACKGROUND: Korean infants born at 32-35 weeks gestational age (wGA) receive palivizumab prophylaxis to prevent respiratory syncytial virus hospitalization (RSVH) if they are born during the RSV season and have a sibling. The aim of this study was to evaluate the impact of using the International Risk Scoring Tool (IRST) to target prophylaxis in Korea. METHODS: The IRST includes 3 risk factors: birth 3 months before to 2 months after the RSV season starts; smokers in the household and/or smoking while pregnant; and, siblings/daycare. First, the accuracy of the Korean guidelines to predict RSVH was compared to that of the IRST using a historic dataset of 13,475 infants born 32-35 wGA. Second, a published cost-utility model was adapted using Korean-specific parameters for costs (2022) and resource use to assess the cost-effectiveness of palivizumab versus no prophylaxis guided either by the Korean guidelines or the IRST. RESULTS: Using the Korean guidelines identified 26.9% of RSVHs, with an area under the receiver operating characteristic curve of 0.512. The corresponding results for infants assessed at moderate- to high-risk by the IRST were 85.1% and 0.773, respectively. The incremental cost per quality-adjusted life year (QALY) for prophylaxis versus no prophylaxis was ₩29,674,102 (USD22,977) using the Korean guidelines, with a 67.0% probability for cost-effectiveness against a willingness-to-pay threshold of ₩41,655,203 (USD32,255). For the IRST, it was ₩26,265,142 (USD20,338)/QALY and 70.8% probability. CONCLUSIONS: Adoption of the IRST in Korea would provide greater protection of the most vulnerable infants born 32-35 wGA against RSVH whilst improving cost-effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".