Bibliographic record
Abstract
PURPOSE OF REVIEW: Transplant patients are uniquely predisposed to emerging infections for a number of reasons. Two outbreaks, West Nile virus and severe acute respiratory syndrome, have recently provided important lessons on how transplant patients are affected, and how transplant programmes must adapt and evolve in the face of emerging infections. An update of emerging infections in transplant patients, using West Nile virus and severe acute respiratory syndrome as specific examples, is summarized here. RECENT FINDINGS: Exogenous immunosuppression, specific allograft factors, and extensive contact with the healthcare system all predispose transplant patients to emerging infections. Transplant patients may acquire West Nile virus through blood transfusion, donor transmission, or community exposure. Seroprevalence data in transplant populations suggest the risk of severe neurological disease is several fold higher in transplant recipients who acquire West Nile virus compared with immunocompetent individuals. Prevention strategies are critical in this population. These include nucleic acid testing of blood products and potentially also screening organ donors in a similar manner. During the outbreak of severe acute respiratory syndrome, transplant patients with severe and rapidly progressive disease were reported. Higher viral burdens appeared to be present in transplant patients and may have implications for the increased infectivity of these patients. Transplant programmes in severe acute respiratory syndrome areas were also adversely affected because of donor concerns, recipient issues and resource problems. SUMMARY: Transplant patients are uniquely predisposed to emerging infections. Lessons learned from West Nile virus and severe acute respiratory syndrome in transplantation should be applicable to future outbreaks of other emerging infectious diseases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".