A pragmatic individually randomized trial to evaluate bivalent RSV prefusion F protein–based vaccine effectiveness for preventing RSV hospitalizations in adults aged 60 years or above (DAN-RSV): Rationale and trial design
Bibliographic record
Abstract
BACKGROUND: Respiratory syncytial virus (RSV) can cause serious illness in older adults and those with chronic conditions. While the bivalent RSVpreF vaccine has been shown to protect against RSV-related respiratory tract disease, its impact on severe RSV-related and broader cardiorespiratory hospitalizations remains untested in a fully powered randomized trial. This pragmatic, individually randomized, open-label, parallel-group trial aims to evaluate RSVpreF vaccine effectiveness (VE) in reducing the risk of RSV-related and all-cause cardiorespiratory hospitalizations in adults aged 60 and older. METHODS: DAN-RSV is randomizing Danish adults 1:1 to receive either RSVpreF or no RSV vaccine. The trial uses nationwide registries for recruitment, where eligible citizens are identified and invited via the national electronic messaging system and can provide electronic informed consent remotely. Baseline, safety, and outcome data are collected through the national health registries using the civil registration number provided at consent. Up to 130,000 participants will be enrolled during the 2024/2025 winter season. The primary objective is to assess vaccine effectiveness (VE) against RSV-related respiratory tract disease hospitalization. Secondary endpoints include RSV-related and all-cause lower respiratory tract disease hospitalizations, RSV-related and all-cause cardiorespiratory hospitalizations, and all-cause death. CONCLUSION: DAN-RSV is an innovative trial combining the gold standard of individual randomization with pragmatic data collection via centralized health records and national health registries. This design offers a feasible approach to assess the impact of RSVpreF on clinically meaningful cardio-respiratory outcomes in adults ≥60 years in a real-world setting - while minimizing bias through use of randomization. The results will support cost-effectiveness analyses and inform future vaccination policies. TRIAL REGISTRATION: NCT06684743, registered November 9, 2024 (https://clinicaltrials.gov/study/NCT06684743).
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".