The burden of COVID-19 mortality among solid organ transplant recipients in the United States
Bibliographic record
Abstract
Solid organ transplant recipients (SOTRs) have a heightened risk of adverse coronavirus disease 2019 (COVID-19) outcomes because of immunosuppression and medical comorbidity. We quantified the burden of COVID-19 mortality in United States (US) SOTRs. A sample of deaths documented in the US solid organ transplant registry from June 2020 through December 2022 was linked to the National Death Index to identify COVID-19 deaths and weighted to represent all SOTR deaths during the study period. Among 505 757 SOTRs, 57 575 deaths occurred, and based on the linkage, 12 396 (21.5%) were due to COVID-19. COVID-19 mortality was higher in males (mortality rate ratio [MRR]: 1.13), SOTRs aged 65 years and older (MRR: 1.50 in ages 65-74 vs ages 55-64 years), and non-Hispanic Black and Hispanic SOTRs (MRRs: 1.55 and 1.79 vs non-Hispanic White SOTRs). Kidney and lung recipients had the highest COVID-19 mortality, followed by heart, and then liver recipients. COVID-19 mortality also varied over time and across US states. Overall, SOTRs had a 7-fold increased risk of COVID-19 death compared to the US general population. SOTRs comprised 0.13% of the US population but accounted for 1.46% of all US COVID-19 deaths. SOTRs experience greatly elevated COVID-19 mortality. Clinicians should continue to prioritize COVID-19 prevention and treatment in this high-risk population.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".