Frailty Is Associated With Increased Hemagglutination-Inhibition Titers in a 4-Year Randomized Trial Comparing Standard- and High-Dose Influenza Vaccination
Bibliographic record
Abstract
BACKGROUND: Although high-dose (HD) vaccines have been reported to stimulate higher antibody responses compared with standard-dose (SD) influenza vaccines, there have been limited studies on the impact of frailty on such responses. METHODS: We conducted a randomized, double-blind trial (2014/2015 to 2017/2018) of SD versus HD trivalent split-virus vaccine (Fluzone) in 612 study participants aged 65+ over 4 influenza seasons. Hemagglutination inhibition antibody titers for influenza H1N1, H3N2, and B vaccine subtypes were measured at baseline and at 4, 10, and 20 weeks postvaccination and frailty was measured using a validated frailty index. RESULTS: Geometric mean antibody titers were significantly higher in HD compared with SD vaccine recipients for all influenza subtypes at all time points postvaccination. However, frailty was positively correlated with 4-week titers and was associated with increased odds of being a vaccine responder. For influenza A subtypes, this was mostly limited to HD recipients. CONCLUSIONS: Frailty was associated with higher titers and increased antibody responses at 4 weeks after influenza vaccination, which was partially dependent on vaccine dosage. Chronic inflammation or dysregulated immunity, both of which are commonly observed with frailty, may be responsible, but it requires further investigation.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".