Global molecular diversity of RSV – the “INFORM RSV” study
Bibliographic record
Abstract
BACKGROUND: Respiratory syncytial virus (RSV) is a global cause of severe respiratory morbidity and mortality in infants. While preventive and therapeutic interventions are being developed, including antivirals, vaccines and monoclonal antibodies, little is known about the global molecular epidemiology of RSV. INFORM is a prospective, multicenter, global clinical study performed by ReSViNET to investigate the worldwide molecular diversity of RSV isolates collected from children less than 5 years of age. METHODS: The INFORM study is performed in 17 countries spanning all inhabited continents and will provide insight into the molecular epidemiology of circulating RSV strains worldwide. Sequencing of > 4000 RSV-positive respiratory samples is planned to detect temporal and geographical molecular patterns on a molecular level over five consecutive years. Additionally, RSV will be cultured from a subset of samples to study the functional implications of specific mutations in the viral genome including viral fitness and susceptibility to different monoclonal antibodies. DISCUSSION: The sequencing and functional results will be used to investigate susceptibility and resistance to novel RSV preventive or therapeutic interventions. Finally, a repository of globally collected RSV strains and a database of RSV sequences will be created.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".