Respiratory viral infections including COVID-19 in solid organ transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Respiratory viral infections are prevalent and contribute to significant morbidity and mortality among solid organ transplant (SOT) recipients. We review updates from literature on respiratory viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in the SOT recipient. RECENT FINDINGS: With the wider availability and use of molecular diagnostic tests, our understanding of the epidemiology and impact of respiratory viruses in the SOT population continues to expand. While considerable attention has been given to the coronavirus disease 2019 (COVID-19) pandemic, the advances in prevention and treatment strategies of SARS-CoV-2 offered valuable insights into the development of new therapeutic options for managing other respiratory viruses in both the general and SOT population. SUMMARY: Respiratory viruses can present with a diverse range of symptoms in SOT recipients, with potentially associated acute rejection and chronic lung allograft dysfunction in lung transplant recipients. The epidemiology, clinical presentations, diagnostic approaches, and treatment and preventive strategies for clinically significant RNA and DNA respiratory viruses in SOT recipients are reviewed. This review also covers novel antivirals, immunologic therapies, and vaccines in development for various community-acquired respiratory viruses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".