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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".