The use of Quantiferon-TB gold in-tube test in screening latent tuberculosis among Saudi Arabia dialysis patients
Bibliographic record
Abstract
BACKGROUND AND AIM: Screening for tuberculosis (TB) is a key strategy for controlling infection. This study aimed to detect latent TB among dialysis patients. METHODS: This is a prospective study conducted in King Saud University, Riyadh involving hemodialysis (HD) and peritoneal dialysis (PD) patients aged ≥18 years. Patients were screened for latent TB infection (LTBI) using both TBskin test (TST) and QuantiFERONTB Gold In-Tube test (QFT-GIT). All participants were followed-up clinically and radiologically every 3 months for 2 years. RESULTS: A total of 243 (181 HD and 62 PD) patients were included and 112(46.1%) were males. 45.3% showed positive QFT in HD patients with sensitivity of 91.7%, specificity of 71.4%, positive predictive value (PPV) of 19.5%, and negative predictive value (NPV) of 91.1%. TST results in HD showed that positive TST was 17.4%, sensitivity was 63.2%, specificity was 95.5%, PPV was 51.5%, and NPV was 91.1%. Five (8.1%) showed positive QFT in PD patients with sensitivity of 7.7%, specificity of 91.8%, PPV of 6.6%, and NPV of 92.3%. TST results in PD showed that positive TST was 9.8%, sensitivity was 35.7%, specificity was 97.9%, PPV was 55.8%, and NPV was 93.3%. Previous TB infection was significantly correlated with QFT only in HD patients, but significantly associated with TST in both HD and PD patients. Also in HD, QFT was significantly associated with TST (P = 0.043). CONCLUSIONS: Due to high variability of QFT-GIT sensitivity, we recommend its use for its NPV and to use either TST or QFT in screening latent TB.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".