Respiratory syncytial virus hospitalisation by chronological month of age and by birth month in infants
Bibliographic record
Abstract
Understanding the distribution of respiratory syncytial virus (RSV) disease burden by more granular age bands in infants is necessary for optimising infant RSV immunisation strategies. Using a Bayesian model, we synthesised published data from a systematic literature review and unpublished data shared by international collaborators for estimating the distribution of infant RSV hospitalisations by month of age. Based on local RSV seasonality data, we further developed and validated a web-based prediction tool for estimating infant RSV hospitalisation distribution by birth month. Although RSV hospitalisation burden mostly peaked at the second month of life and was concentrated in infants under six months globally, substantial variations were noted in the age distribution of RSV hospitalisation among infants born in different months. Passive immunisation strategies should ideally be tailored to the local RSV disease burden distribution by age and birth month to maximise their per-dose effectiveness before a universal immunisation can be achieved. Passive immunisation for respiratory syncytial virus for infants is recommended by the World Health Organization but products currently available have limited duration of protection. Here, the authors investigate the age distribution of infant hospitalisation for respiratory syncytial virus to inform optimal timing of immunisation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| 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".