Serial Testing of Health Care Workers for Tuberculosis Using Interferon-γ Assay
Bibliographic record
Abstract
RATIONALE: Although interferon-gamma (IFN-gamma) assays are promising alternatives to the tuberculin skin test (TST), their serial testing performance is unknown. OBJECTIVE: To compare TST and IFN-gamma conversions and reversions in healthcare workers. METHODS: We prospectively followed-up 216 medical and nursing students in India who underwent baseline and repeat testing (after 18 mo) with TST and QuantiFERON-TB Gold In-Tube (QFT). TST conversions were defined as reactions greater than or equal to 10 mm, with increments of 6 or 10 mm over baseline. QFT conversions were defined as baseline IFN-gamma less than 0.35 and follow-up IFN-gamma greater than or equal to 0.35 or 0.70 IU/ml. QFT reversions were defined as baseline IFN-gamma greater than or equal to 0.35 and follow-up IFN-gamma less than 0.35 IU/ml. RESULTS: Of the 216 participants, 48 (22%) were TST-positive, and 38 (18%) were QFT-positive at baseline. Among 147 participants with concordant baseline negative results, TST conversions occurred in 14 (9.5%; 95% confidence interval [CI] = 5.3-15.5) using the 6 mm increment definition, and 6 (4.1%; 95% CI = 1.5-8.7) using the 10 mm increment definition. QFT conversions occurred in 17/147 participants (11.6%; 95% CI = 6.9-17.9) using the definition of IFN-gamma greater than or equal to 0.35 IU/ml, and 11/147 participants (7.5%; 95% CI = 3.8-13.0) using IFN-gamma greater than or equal to 0.70 IU/ml. Agreement between TST (10 mm increment) and QFT conversions (>or= 0.70 IU/ml) was 96% (kappa = 0.70). QFT reversions occurred in 2/28 participants (7%) with baseline concordant positive results, as compared with 7/10 participants (70%) with baseline discordant results (p < 0.001). CONCLUSIONS: IFN-gamma assay shows promise for serial testing, but repeat results need to be interpreted carefully. To meaningfully interpret serial results, the optimal thresholds to distinguish new infections from nonspecific variations must be determined.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".