Nirsevimab Effectiveness Against Medically Attended Respiratory Syncytial Virus Illness and Hospitalization Among Alaska Native Children — Yukon-Kuskokwim Delta Region, Alaska, October 2023–June 2024
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Respiratory syncytial virus (RSV) is a leading cause of hospitalization among young children. Historically, American Indian and Alaska Native (AI/AN) children have experienced high rates of RSV-associated hospitalization. In August 2023, a preventive monoclonal antibody (nirsevimab) was recommended for all infants aged <8 months (born during or entering their first RSV season) and for children aged 8-19 months (entering their second RSV season) who have increased risk for severe RSV illness, including all AI/AN children. This evaluation in Alaska's Yukon-Kuskokwim Delta region estimated nirsevimab effectiveness among AI/AN children in their first or second RSV seasons during 2023-2024. Among 472 children with medically attended acute respiratory illness (ARI), 48% overall had received nirsevimab ≥7 days earlier (median = 91 days before the ARI-related visit). For children in their first RSV season (292), nirsevimab effectiveness was 76% (95% CI = 42%-90%) against medically attended RSV illness and 89% (95% CI = 32%-98%) against RSV hospitalization. For children in their second RSV season (180), effectiveness against medically attended RSV illness was 88% (95% CI = 48%-97%). Nirsevimab is effective for preventing severe RSV illness among infants entering their first RSV season and children entering their second season with increased risk for severe RSV, including all AI/AN children.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it