The relative vaccine effectiveness of high-dose vs standard-dose influenza vaccines in preventing hospitalization and mortality: A meta-analysis of evidence from randomized trials
Bibliographic record
Abstract
OBJECTIVES: To summarize current evidence of high-dose influenza vaccine (HD-IV) vs standard-dose (SD-IV) regarding severe clinical outcomes. METHODS: A prespecified meta-analysis was conducted to assess relative vaccine effectiveness (rVE) of HD-IV vs SD-IV in reducing the rates of (1) pneumonia and influenza (P&I) hospitalization, (2) all hospitalizations, and (3) all-cause death in adults ≥ 65 years in randomized controlled trials. Pooled effect sizes were estimated using fixed-effects models with the inverse variance method. RESULTS: Five randomized trials were included encompassing 105,685 individuals. HD-IV vs SD-IV reduced P&I hospitalizations (rVE: 23.5 %, [95 %CI: 12.3 to 33.2]). HD-IV vs SD-IV also reduced rate of all-cause hospitalizations (rVE: 7.3 %, [95 %CI: 4.5 to 10.0]). No significant differences were observed in death rates (rVE = 1.6 % ([95 %CI: -2.0 to 5.0]) in HD-IV vs SD-IV. Sensitivity analyses omitting trials with participants sharing the same comorbidity, trials with ≥ 100 events, and random-effects models provided comparable estimates for all outcomes. CONCLUSIONS: HD-IV reduced the incidence of P&I and all-cause hospitalization vs SD-IV in adults ≥ 65 years in randomized trials, through no significant difference was observed in all-cause death rates. These findings, supported by evidence from several randomized studies, can benefit from replication in a fully powered, individually randomized trial.
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How this classification was reachedexpand
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 | 0.038 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".