Transplant Infectious Diseases: A Review of the Scientific Registry of Transplant Recipients Published Data
Bibliographic record
Abstract
The Scientific Registry of Transplant Recipients (SRTR) serves to collect data on organ transplants performed in the United States. Although the infectious diseases data are limited and include mostly pretransplant serologies and other nonspecific infection-related outcomes, this multicenter data collection allows for insightful national data and the ability to monitor trends over time. We reviewed the published concise reports for each organ type in SRTR reports containing data from 2005 to 2014, and summarized our findings with respect to cytomegalovirus (CMV), Epstein-Barr virus, posttransplant lymphoproliferative disorder (PTLD), hepatitis B virus (HBV), hepatitis C virus (HCV), HIV, general infection, and prophylaxis. Our review highlights a few developments. While rates of donor-recipient CMV serology combinations remain fairly constant over time, there are generally more seronegative donors and recipients among living donor transplants. There has been a reduction in PTLD for pediatric transplant recipients. There has also been a slight reduction in anti-HBV core antibody-positive donor organs and stable reporting of HCV-positive donor organs and HIV-positive recipients.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".