Comparison of Quantiferon-TB Gold With Tuberculin Skin Test for Detecting Latent Tuberculosis Infection Prior to Liver Transplantation
Bibliographic record
Abstract
Screening for latent tuberculosis infection (LTBI) is recommended prior to organ transplantation. The Quantiferon-TB Gold assay (QFT-G) may be more accurate than the tuberculin skin test (TST) in the detection of LTBI. We prospectively compared the results of QFT-G to TST in patients with chronic liver disease awaiting transplantation. Patients were screened for LTBI with both the QFT-G test and a TST. Concordance between test results and predictors of a discordant result were determined. Of the 153 evaluable patients, 37 (24.2%) had a positive TST and 34 (22.2%) had a positive QFT-G. Overall agreement between tests was 85.1% (kappa= 0.60, p < 0.0001). Discordant test results were seen in 12 TST positive/QFT-G negative patients and in 9 TST negative/QFT-G positive patients. Prior BCG vaccination was not associated with discordant test results. Twelve patients (7.8%), all with a negative TST, had an indeterminate result of the QFT-G and this was more likely in patients with a low lymphocyte count (p = 0.01) and a high MELD score (p = 0.001). In patients awaiting liver transplantation, both the TST and QFT-G were comparable for the diagnosis of LTBI with reasonable concordance between tests. Indeterminate QFT-G result was more likely in those with more advanced liver disease.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".