Efficacy and safety of COVID-19 vaccination in solid organ transplant recipients: A systematic review and network meta-analysis
Bibliographic record
Abstract
The impact of COVID-19 vaccination on clinical outcomes in solid organ transplant (SOT) recipients remains unclear. This systematic review and network meta-analysis sought to assess the efficacy and safety of COVID-19 vaccination in SOT recipients. We searched 6 databases from inception to March 1, 2024 for randomized controlled trials (RCTs) and observational studies evaluating different COVID-19 vaccination strategies in SOT recipients. Based on patient-important outcomes, we performed frequentist random-effects pairwise meta-analyses and network meta-analyses, separating RCTs and nonrandomized evidence, and used the Grading of Recommendation, Assessment, Development, and Evaluation approach to assess our certainty in the evidence. We included 6 RCTs (N = 814) and 43 observational studies (N = 125 199). Overall, there is a paucity of randomized evidence evaluating COVID-19 vaccines in SOT recipients. The nonrandomized evidence evaluating COVID-19 vaccination strategies patient-important outcomes, including COVID-19 infection, mortality, hospitalization, ICU admission, and rejection, demonstrated low to very low certainty due to the included studies' risk of bias. Throughout the COVID-19 pandemic, clinicians and SOT recipients worked with minimal, very low-quality evidence in relation to COVID-19 vaccines in this population. In the instance of future public health emergencies, clinicians and researchers should collaborate closely with patient partners to ensure there is sufficient evidence in the transplant population on patient-important outcomes.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.045 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".