Comparison of QuantiFERON-TB Gold In-Tube to Tuberculin Skin Test for the Diagnosis of Active Tuberculosis (TB) in India - Preliminary Analysis
Bibliographic record
Abstract
Background: Conventional diagnostic tests for TB have limitations. Tuberculin skin test is non-specific and may be influenced by the BCG vaccine. New immunologic assays like the QuantiFERON-TB Gold in-tube (QFT) assay (Cellestis, Australia) detect interferon-gamma (IFN-γ) production in whole blood samples in response to stimulation with TB antigens. The role of QFT and Tuberculin skin test (TST) in the diagnosis of active TB among adults in high burden countries is not clear. Methods: We prospectively evaluated pulmonary and extrapulmonary TB suspects from a tertiary center in India, in a blinded comparison of new diagnostic tests. We aim to recruit 200 patients for the study. The blood samples collected from the patients were processed as per manufacturers instructions. The cut off for positivity used was 0.35 IU/ml. TST was performed using 2TU dose and 10 mm or greater was considered positive. Both were evaluated against a combined gold standard of solid (Lowenstein Jensen) and liquid (BACTEC 460 TB) culture. Results: To date the results of QFT and culture for 51 patients are available. Four indeterminate results were not included. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of QFT for culture positive TB were 81% (54–95), 67% (47–82), 57% (35–76) and 87% (65–96) respectively. 52 patients had TST and culture results available. The sensitivity, specificity, PPV and NPV of TST for culture positive TB were 68% (45–85), 50% (32–68), 50% (32–68) and 68% (45–85) respectively. Conclusion: QFT has adequate sensitivity but poor specificity to detect active TB in India. QFT shows a trend to better sensitivity than TST. As expected, latent TB infection causes false positives. QFT is a single visit test with good negative predictive value but should not be used alone to rule out active 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".