mRNA Vaccine Effectiveness Against Coronavirus Disease 2019 Hospitalization Among Solid Organ Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: The study objective was to evaluate 2- and 3-dose coronavirus disease 2019 (COVID-19) mRNA vaccine effectiveness (VE) in preventing COVID-19 hospitalization among adult solid organ transplant (SOT) recipients. METHODS: We conducted a 21-site case-control analysis of 10 425 adults hospitalized in March to December 2021. Cases were hospitalized with COVID-19; controls were hospitalized for an alternative diagnosis (severe acute respiratory syndrome coronavirus 2-negative). Participants were classified as follows: SOT recipient (n = 440), other immunocompromising condition (n = 1684), or immunocompetent (n = 8301). The VE against COVID-19-associated hospitalization was calculated as 1-adjusted odds ratio of prior vaccination among cases compared with controls. RESULTS: Among SOT recipients, VE was 29% (95% confidence interval [CI], -19% to 58%) for 2 doses and 77% (95% CI, 48% to 90%) for 3 doses. Among patients with other immunocompromising conditions, VE was 72% (95% CI, 64% to 79%) for 2 doses and 92% (95% CI, 85% to 95%) for 3 doses. Among immunocompetent patients, VE was 88% (95% CI, 87% to 90%) for 2 doses and 96% (95% CI, 83% to 99%) for 3 doses. CONCLUSIONS: Effectiveness of COVID-19 mRNA vaccines was lower for SOT recipients than immunocompetent adults and those with other immunocompromising conditions. Among SOT recipients, vaccination with 3 doses of an mRNA vaccine led to substantially greater protection than 2 doses.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".