Efficacy of influenza vaccination in HIV‐positive patients: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: International treatment guidelines recommend that HIV-positive patients be vaccinated for influenza annually. Evidence supporting this recommendation is limited. We assessed the efficacy of influenza vaccines in preventing influenza in HIV-positive patients through a systematic review and meta-analysis. METHODS: We searched 10 electronic databases independently, in duplicate (from inception to June 2007). We extracted data on study design, population characteristics and outcomes related to influenza symptoms and antibody titres. We pooled data using a random effects model and conducted sensitivity analyses to evaluate heterogeneity. RESULTS: We included four studies. Three studies were evaluable for meta-analysis and yielded a pooled relative risk reduction (RRR) of 66% [95% confidence interval (CI) 36-82%; I(2)=73%]. One case-control study yielded an odds ratio of 1.98 (95% CI 0.75-5.20). When we assessed heterogeneity according to study design, we found that the study of the highest quality, a randomized clinical trial (RCT), yielded the most conservative estimate (RRR 41%; 95% CI 2-64%). INTERPRETATION: Evidence supporting influenza vaccination of HIV-positive individuals is limited, poorly quantified and characterized by substantial methodological shortcomings. A reasonable estimate of influenza vaccination effectiveness in HIV-positive patients cannot be derived from these data. There is an urgent need for randomized trials to guide policy and clinical practice.
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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".