Comparison of Two Interferon-γ Assays and Tuberculin Skin Test for Tracing Tuberculosis Contacts
Bibliographic record
Abstract
BACKGROUND: The tuberculin skin test (TST) has low specificity. QuantiFERON-TB Gold (QFT-G) and T-SPOT.TB are based on interferon (IFN)-gamma responses to Mycobacterium tuberculosis-specific antigens. A novel in-tube format of QFT-G (QFT-GIT) offers logistical advantages. OBJECTIVE: To compare TST, QFT-GIT, and T-SPOT.TB in bacillus Calmette-Guérin unvaccinated contacts and correlate results with measures of recent exposure. METHODS: When a supermarket employee with smear-positive tuberculosis had infected most close contacts, a contact investigation among more than 20,000 customers was performed. We recruited subjects randomly on the day of TST administration (n = 469) and subjects with TST of more than 0 mm on the day of TST reading (n = 316). QFT-GIT and T-SPOT.TB were performed. Demographic data and measures of exposure were collected. TST results were analyzed at a cutoff of 10 or 15 mm. Blood tests were interpreted following the manufacturers' criteria and by varying cutoff levels. RESULTS: Among 785 study participants, TST results were associated with age, whereas positive IFN-gamma responses were significantly associated with cumulative shopping time, most markedly for QFT-GIT. Among participants with a TST of 15 mm or greater, sensitivity of QFT-GIT and T-SPOT.TB was 42.2 and 51.3%, respectively. Interassay agreement was 89.6% (kappa = 0.59). By varying cutoff values, agreement between the IFN-gamma assays was optimal at 93.6% (kappa = 0.71) using a cutoff of 0.20 IU/ml for QFT-GIT and 13 spots for T-SPOT.TB. CONCLUSIONS: Blood test results were associated with exposure, whereas the TST was not. A possible lack of sensitivity of IFN-gamma assays in detecting individuals with TST of 15 mm or greater, despite negative bacillus Calmette-Guérin vaccination status, warrants further investigation into alternative cutoff values.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".