The Diagnostic Accuracy of Tests for Latent Tuberculosis Infection in Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND: Reactivation of latent Mycobacterium tuberculosis infection is an important health concern for patients on hemodialysis because of their immunosuppressed state and in kidney transplant patients receiving immunosuppressive therapy to prevent organ rejection. There are several tests available to determine the presence of latent tuberculosis infection: the tuberculin skin test (TST), QuantiFERON-TB Gold (QFT-G), and T-SPOT.TB. The objective of this study is to evaluate the diagnostic accuracy of these tests in determining latent tuberculosis infection in the hemodialysis population. METHODS: The study design was a systematic review. We selected studies with adequate information to ascertain test sensitivity or specificity of the TST, QFT-G, and TSPOT.TB with regards to determining latent tuberculosis infection in the hemodialysis population. RESULTS: One hundred two articles were selected for full review, and 17 were included in the meta-analysis. The TST had a pooled sensitivity of 31% (26%-36%, 95% confidence interval) and specificity of 63% (60%-65%) across eight studies. The QFT-G test had a pooled sensitivity of 53% (46%-59%) and specificity of 69% (65%-72%) across nine studies. The T-SPOT.TB test had a pooled sensitivity of 50% (42%-59%) and specificity of 67% (61%-73%) across three studies. CONCLUSION: The QFT-G and the T-SPOT.TB tests were more sensitive than the TST for diagnosis of latent tuberculosis infection in patients on hemodialysis while offering a comparable level of specificity. This systematic review calls into question the practice of using the TST to screen in this population, especially in patients considered for kidney transplantation.
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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 | 0.032 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.017 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".