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 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 | 0.033 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.065 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".