The comparative effectiveness of adjuvanted and unadjuvanted trivalent inactivated influenza vaccine (TIV) in the elderly
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Influenza is associated with a high mortality and morbidity in older adults. Vaccination remains the most effective method of preventing influenza and its consequences, however, vaccine effectiveness decreases with increasing age and increasing immunosenescence. In older adults, immunogenicity studies suggest an MF59 adjuvanted influenza vaccine (ATIV, Fluad(®)) may help. METHODS: We evaluated the comparative effectiveness of ATIV, and unadjuvanted trivalent influenza vaccine (TIV) in reducing laboratory confirmed influenza in the elderly. Elderly in three health authorities during winter 2011-12 were included in a community based case control study design. Cases tested positive and controls tested negative for influenza. Subjects with known immunosuppression were excluded. Logistic regression was used to calculate the odds ratio of vaccination (vs. no vaccination) in cases and controls. ATIV and TIV effectiveness was described. RESULTS: A total of 282 eligible participants were enrolled (84 cases). Almost half (136) were in a long term care facility and were 85 years of age or older (132) vaccine effectiveness decreased with increasing age. In a variety of multivariate analyses, ATIV was significantly protective at around 60% (p=0.02), with only residence in long term care and health authority also significant. Vaccine effectiveness increased in non-long term care residents. In multivariate analyses TIV was ineffective. CONCLUSION: An MF59 adjuvanted vaccine provided significantly improved protection against influenza in the elderly.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it