Cough Aerosol Cultures of Mycobacterium tuberculosis: Insights on TST / IGRA Discordance and Transmission Dynamics
Bibliographic record
Abstract
The diagnosis of latent tuberculosis (TB) infection (LTBI) is complicated by the absence of a gold standard. Discordance between tuberculin skin tests (TST) and interferon gamma release assays (IGRA) occurs in 10-20% of individuals, but the underlying mechanisms are poorly understood.We analyzed data from a prospective household contact study that included cough aerosol culture results from index cases, environmental and contact factors. We assessed contacts for LTBI using TST and IGRA at baseline and six weeks. We examined TST/IGRA discordance in qualitative and quantitative analyses, and used multivariable logistic regression analysis with generalized estimating equations to analyze predictors of discordance.We included 96 TB patients and 384 contacts. Discordance decreased from 15% at baseline to 8% by six weeks. In adjusted analyses, discordance was related to less crowding (p = 0.004), non-cavitary disease (OR 1.41, 95% CI: 1.02-1.96; p = 0.03), and marginally with BCG vaccination in contacts (OR 1.40, 95% CI: 0.99-1.98, p = 0.06).We observed significant individual variability and temporal dynamism in TST and IGRA results in household contacts of pulmonary TB cases. Discordance was associated with a less intense infectious exposure, and marginally associated with a BCG-mediated delay in IGRA conversion. Cough aerosols provide an additional dimension to the assessment of infectiousness and risk of infection in contacts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".