Comparative Evaluation of GeneXpert With Ziehl-Neelsen (ZN) Stain in Samples of Suspected Tuberculosis Cases at a Tertiary Care Teaching Hospital in Central India
Bibliographic record
Abstract
Background Tuberculosis (TB) remains a significant public health challenge, particularly in developing countries, where delayed diagnosis contributes to ongoing transmission. Ziehl-Neelsen (ZN) smear microscopy, commonly used for TB diagnosis, has limitations in sensitivity, especially in cases of extrapulmonary tuberculosis (ETB). The GeneXpert Mycobacterium tuberculosis (MTB)/rifampicin (RIF) assay, a molecular diagnostic tool, offers rapid and accurate detection of MTB and RIF resistance. This study aimed to compare the diagnostic efficacy of GeneXpert MTB/RIF with ZN staining in detecting pulmonary tuberculosis (PTB) and ETB. Methods A prospective study was conducted over two years (April 2022 to April 2024) at a tertiary care teaching hospital in India. A total of 319 clinical samples from patients with suspected PTB and ETB were analyzed. Samples underwent ZN staining and GeneXpert MTB/RIF assay. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using mycobacterial culture as the gold standard. The chi-square test was employed to compare diagnostic accuracy, with a p-value of <0.05 considered statistically significant. Results Of the 319 samples, ZN staining was positive in 18.2% of cases, while GeneXpert was positive in 21.6%. GeneXpert demonstrated a perfect sensitivity of 100% and a specificity of 98.81%, compared to ZN staining’s sensitivity of 84.85% and specificity of 99.21%. GeneXpert showed superior performance in detecting TB in both pulmonary and extrapulmonary samples, with a statistically significant difference (p<0.001). Additionally, GeneXpert identified six cases of RIF resistance. Conclusion The GeneXpert MTB/RIF assay outperforms ZN staining in diagnosing TB, offering higher sensitivity and comparable specificity. Its ability to detect RIF resistance adds significant clinical value. Despite its cost, the integration of GeneXpert into routine diagnostic workflows, particularly in high TB prevalence areas, is recommended to enhance early detection and treatment, thereby reducing TB transmission.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".