Are interferon-γ release assays useful for diagnosing active tuberculosis in a high-burden setting?
Bibliographic record
Abstract
Although interferon-γ release assays (IGRAs) are intended for diagnosing latent tuberculosis (TB), we hypothesised that in a high-burden setting: 1) the magnitude of the response when using IGRAs can distinguish active TB from other diagnoses; 2) IGRAs may aid in the diagnosis of smear-negative TB; and 3) IGRAs could be useful as rule-out tests for active TB. We evaluated the accuracy of two IGRAs (QuantiFERON®-TB Gold In-tube (QFT-GIT) and T-SPOT®.TB) in 395 patients (27% HIV-infected) with suspected TB in Cape Town, South Africa. IGRA sensitivity and specificity (95% CI) were 76% (68-83%) and 42% (36-49%) for QFT-GIT and 84% (77-90%) and 47% (40-53%) for T-SPOT®.TB, respectively. Although interferon-γ responses were significantly higher in the TB versus non-TB groups (p<0.0001), varying the cut-offs did not improve discriminatory ability. In culture-negative patients, depending on whether those with clinically diagnosed TB were included or excluded from the analysis, the negative predictive value (NPV) of QFT-GIT, T-SPOT®.TB and chest radiograph in smear-negative patients varied between 85 and 89, 87 and 92, and 98% (for chest radiograph), respectively. Overall accuracy was independent of HIV status and CD4 count. In a high-burden setting, IGRAs alone do not have value as rule-in or -out tests for active TB. In smear-negative patients, chest radiography had better NPV even in HIV-infected patients.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".