Serological response following COVID-19 vaccines in patients living with HIV: a dose–response meta-analysis
Bibliographic record
Abstract
To quantify the pooled rate and risk ratio of seroconversion following the uncomplete, complete, or booster dose of COVID-19 vaccines in patients living with HIV. PubMed, Embase and Cochrane library were searched for eligible studies to perform a systematic review and meta-analysis based on PRIMSA guidelines. The pooled rate and risk ratio of seroconversion were assessed using the Freeman-Tukey double arcsine method and Mantel-Haenszel approach, respectively. Random-effects model was preferentially used as the primary approach to pool results across studies. A total of 50 studies involving 7160 patients living with HIV were analyzed. We demonstrated that only 75.0% (56.4% to 89.9%) patients living with HIV achieved a seroconversion after uncomplete vaccination, which improved to 89.3% (84.2% to 93.5%) after complete vaccination, and 98.4% (94.8% to 100%) after booster vaccination. The seroconversion rates were significantly lower compared to controls at all the stages, while the risk ratios for uncomplete, complete, and booster vaccination were 0.87 (0.77 to 0.99), 0.95 (0.92 to 0.98), and 0.97 (0.94 to 0.99), respectively. We concluded that vaccine doses were associated with consistently improved rates and risk ratios of seroconversion in patients living with HIV, highlighting the significance of booster vaccination for patients living with HIV.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.055 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".