Outcome of Latent Tuberculosis Infection in Solid Organ Transplant Recipients Over a 10-Year Period
Bibliographic record
Abstract
BACKGROUND: Screening and therapy of latent tuberculosis infection (LTBI) is recommended in solid organ transplant (SOT). However, there are limited data on the tolerability of LTBI therapy pretransplant and posttransplant. We studied the tolerability of LTBI therapy and effectiveness of a centralized LTBI treatment program in a low-risk population. METHODS: Provincial TB and transplant databases were retrospectively reviewed for LTBI therapy referrals in SOT candidates and recipients over a 10-year period. Using univariate logistic regression, we examined factors associated with failure to complete therapy and followed patients for active TB. RESULTS: From 2001 to 2010, 200/461 SOT candidates referred to the TB program (43.4%) were eligible for therapy for LTBI. Eleven patients refused therapy. The remaining patients (n=189) were initially prescribed isoniazid (73%), rifampin (12.7%), or another regimen (14.3%). Adequate LTBI therapy occurred in 122 (64.5%). Patients who were liver transplant candidates or recipients were less likely to complete therapy than nonliver transplant patients (OR, 0.20; P<0.001) as were patients treated in the posttransplant phase (OR, 0.47; P=0.034). Liver enzyme elevation led to discontinuation of therapy more often in liver transplant candidates and recipients (OR, 10.48; P<0.001) and posttransplant treatment (OR, 3.50; P=0.019). In 599.4 patient-years of follow-up posttransplant (mean, 4.9 year/patient), there were no cases of active TB. CONCLUSION: A centralized referral program for LTBI therapy in transplant candidates is effective to prevent TB reactivation posttransplant. A significant proportion of liver transplant candidates and recipients do not tolerate standard LTBI therapy. Alternative therapies for these patients should be evaluated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".