Randomized controlled trial of influenza vaccine in patients with heart failure to reduce adverse vascular events (IVVE): Rationale and design
Bibliographic record
Abstract
BACKGROUND: Influenza is associated with an increase in the risk of cardiac and other vascular events. Observational data and small randomized trials suggest that influenza vaccination may reduce such adverse vascular events. RESEARCH DESIGN AND METHODS: In a randomized controlled trial patients with heart failure are randomized to receive either inactivated influenza vaccine or placebo annually for 3 years. Patients aged ≥18 years with a clinical diagnosis of heart failure and NYHA functional class II, III and IV are eligible. Five thousand patients from 10 countries where influenza vaccination is not common (Asia, the Middle East, and Africa) have been enrolled. The primary outcome is a composite of the following: cardiovascular death, non-fatal myocardial infarction, non-fatal stroke and hospitalizations for heart failure using standardized criteria. Analyses will be based on comparing event rates between influenza vaccine and control groups and will include time to event, rate comparisons using Poisson methods, and logistic regression. The analysis will be conducted by intention to treat i.e. patients will be analyzed in the group in which they were assigned. Multivariable secondary analyses to assess whether variables such as age, sex, seasonality modify the benefits of vaccination are also planned for the primary outcome. CONCLUSION: This is the largest randomized trial to test if influenza vaccine compared to control reduces adverse vascular events in high risk individuals. TRIAL REGISTRATION NUMBER: Clinicaltrials.govNCT02762851.
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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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".