Longitudinal outcomes of COVID-19 in solid organ transplant recipients from 2020 to 2023
Bibliographic record
Abstract
Data regarding coronavirus disease 2019 (COVID-19) outcomes in solid organ transplant recipients (SOTr) across severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) waves, including the impact of different measures, are lacking. This cohort study, conducted from March 2020 to May 2023 in Toronto, Canada, aimed to analyze COVID-19 outcomes in 1975 SOTr across various SARS-CoV-2 waves and assess the impact of preventive and treatment measures. The primary outcome was severe COVID-19, defined as requiring supplemental oxygen, with secondary outcomes including hospitalization, length of stay, intensive care unit (ICU) admission, and 30-day and 1-year all-cause mortality. SARS-CoV-2 waves were categorized as Wildtype/Alpha/Delta (318 cases, 16.1%), Omicron BA.1 (268, 26.2%), Omicron BA.2 (268, 13.6%), Omicron BA.5 (561, 28.4%), Omicron BQ.1.1 (188, 9.5%), and Omicron XBB.1.5 (123, 6.2%). Severe COVID-19 rate was highest during the Wildtype/Alpha/Delta wave (44.6%), and lower in Omicron waves (5.7%-16.1%). Lung transplantation was associated with severe COVID-19 (OR: 4.62, 95% CI: 2.71-7.89), along with rituximab treatment (OR: 4.24, 95% CI: 1.04-17.3), long-term corticosteroid use (OR: 3.11, 95% CI: 1.46-6.62), older age (OR: 1.51, 95% CI: 1.30-1.76), chronic lung disease (OR: 2.11, 95% CI: 1.36-3.30), chronic kidney disease (OR: 2.18, 95% CI: 1.17-4.07), and diabetes (OR: 1.97, 95% CI: 1.37-2.83). Early treatment and ≥3 vaccine doses were associated with reduced severity (OR: 0.29, 95% CI: 0.19-0.46, and 0.35, 95% CI: 0.21-0.60, respectively). Tixagevimab/cilgavimab and bivalent boosters did not show a significant impact. The study concludes that COVID-19 severity decreased across different variants in SOTr. Lung transplantation was associated with worse outcomes and may benefit more from preventive and early therapeutic interventions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".