Serial testing using interferon-γ release assays in nursing students in India
Bibliographic record
Abstract
To the Editor: We have previously shown that Indian healthcare workers have higher prevalence of latent tuberculosis infection (LTBI) and are at increased risk for new infection [1]–[4]. Interferon-γ release assays (IGRAs) have been introduced as an alternative to the tuberculin skin test (TST) for diagnosing LTBI in healthcare workers and other high-risk groups. They have logistical advantages over the TST and will not cross-react with the bacille Calmette Guerin vaccine. IGRAs are now being widely used for screening healthcare workers [5], yet recent reports indicate that switching from TST to IGRAs for the serial testing of healthcare workers may result in increased rates of test conversions and reversions [3], [6]–[8]. Most of these studies are from low tuberculosis (TB) incidence settings, with limited opportunity for nosocomial TB exposure; as a result, the increased conversion rates are considered false-positive test conversions, making it difficult for clinicians to interpret IGRA test conversions in these settings [9]. It remains unclear whether IGRA conversions are associated with TB exposure in high TB incidence settings where unprotected exposure to infectious TB patients is more common among healthcare workers. To evaluate whether IGRA conversions may represent new cases of LTBI, in a high TB incidence setting, in the absence of a gold standard for LTBI, we employed TB exposure as a proxy measure. We established a cohort of nursing students who underwent IGRA testing annually for 3 years at a tertiary level hospital in southern India [1]. Annual rates of conversions and reversions were estimated in this cohort. A relationship between occupational TB exposure and change in continuous interferon-γ (IFN-γ) levels were …
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".