Endoscopic ultrasound-guided biopsy for TB lymphadenopathy: role of PCR and Gene X-pert
Bibliographic record
Abstract
Abstract Tuberculous lymphadenopathy presents a challenging diagnostic scenario, particularly in regions with high tuberculosis (TB) prevalence. Abdominal TB accounts for 10% of extrapulmonary TB cases. Diagnosing abdominal TB is difficult due to nonspecific clinical, radiological, and endoscopic findings. Blood and skin tests for TB exhibit variable sensitivities and specificities; therefore, histopathological diagnosis and molecular testing of tissue samples may provide greater accuracy than blood-based tests. Obtaining tissue samples under ultrasonographic or CT guidance can be difficult and risky, especially in intra-abdominal and mediastinal regions. Endoscopic ultrasound (EUS) and EUS-guided fine-needle biopsy (EUS-FNB) offer a safe technique for obtaining tissue samples for the diagnosis of abdominal and mediastinal TB. Comparative analyses of the Gene X-pert MTB/RIF assay and PCR techniques demonstrate nuanced diagnostic capabilities. Gene X-pert enables rapid molecular detection with high specificity for rifampicin resistance, whereas PCR facilitates molecular amplification of bacterial DNA. EUS provides the critical advantage of real-time imaging and precise tissue sampling. Several studies have found that EUS-FNB yields significantly higher diagnostic accuracy in complex TB presentations, with detection rates ranging from 78 to 92% across various anatomical sites. This review explores the diagnostic efficacy of molecular techniques, specifically polymerase chain reaction (PCR) and the Gene X-pert MTB/RIF assay, in detecting Mycobacterium tuberculosis in tissue samples obtained through EUS-FNB. Additionally, we evaluated the sensitivity, specificity, and rapid detection capabilities of these molecular methods compared to traditional diagnostic techniques. We further discussed whether molecular techniques such as PCR and Gene X-pert provide a powerful diagnostic strategy for TB lymphadenopathy in EUS-FNB tissue samples, thereby overcoming the limitations of conventional diagnostic methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".