After adjusting for bias in meta-analysis seasonal influenza vaccine remains effective in community-dwelling elderly
Bibliographic record
Abstract
OBJECTIVE: To compare the performance of the bias-adjusted meta-analysis to the conventional meta-analysis assessing seasonal influenza vaccine effectiveness among community-dwelling elderly aged 60 years and older. STUDY DESIGN AND SETTING: Systematic literature search revealed 14 cohort studies that met inclusion and exclusion criteria. Laboratory-confirmed influenza, influenza-like illness, hospitalization from influenza and/or pneumonia, and all-cause mortality were study outcomes. Potential biases were identified using bias checklists. The magnitude and uncertainty of biases were assessed by expert opinion. Pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using random effects model. RESULTS: After incorporating biases, overall effect estimates regressed slightly toward no effect, with the largest relative difference between conventional and bias-adjusted ORs for laboratory-confirmed influenza (OR, 0.18; 95% CI: 0.01, 3.00 vs. OR, 0.23; 95% CI: 0.03, 2.04). In most of the studies, CIs widened reflecting uncertainties about the biases. The between-study heterogeneity reduced considerably with the largest reduction for all-cause mortality (I(2) = 4%, P = 0.39 vs. I(2) = 91%, P < 0.01). CONCLUSION: This case study showed that after addressing potential biases influenza vaccine was still estimated effective in preventing hospitalization from influenza and/or pneumonia and all-cause mortality. Increasing the number of assessors and incorporating empirical evidence might improve the new bias-adjustment method.
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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 | 0.144 | 0.248 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.078 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 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".