Diagnosis and Management of Tuberculosis in Transplant Donors: A Donor-Derived Infections Consensus Conference Report
Bibliographic record
Abstract
Mycobacterium tuberculosis is a ubiquitous organism that infects one-third of the world's population. In previous decades, access to organ transplantation was restricted to academic medical centers in more developed, low tuberculosis (TB) incidence countries. Globalization, changing immigration patterns, and the expansion of sophisticated medical procedures to medium and high TB incidence countries have made tuberculosis an increasingly important posttransplant infectious disease. Tuberculosis is now one of the most common bacterial causes of solid-organ transplant donor-derived infection reported in transplant recipients in the United States. Recognition of latent or undiagnosed active TB in the potential organ donor is critical to prevent emergence of disease in the recipient posttransplant. Donor-derived tuberculosis after transplantation is associated with significant morbidity and mortality, which can best be prevented through careful screening and targeted treatment. To address this growing challenge and provide recommendations, an expert international working group was assembled including specialists in transplant infectious diseases, transplant surgery, organ procurement and TB epidemiology, diagnostics and management. This working group reviewed the currently available data to formulate consensus recommendations for screening and management of TB in organ donors.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".