False-positive tuberculin skin tests: what is the absolute effect of BCG and non-tuberculous mycobacteria?
Bibliographic record
Abstract
BACKGROUND: Despite certain drawbacks, the tuberculin skin test (TST) remains in widespread use. Important advantages of the TST are its low cost, simplicity and interpretation based on extensive published literature. However, TST specificity is reduced by bacille Calmette-Guérin (BCG) vaccination and exposure to non-tuberculous mycobacteria (NTM). METHODS: To estimate TST specificity, we reviewed the published literature since 1966 regarding the effect of BCG vaccination and NTM infection on TST. Studies selected included healthy subjects with documented BCG vaccination status, including age at vaccination. Studies of NTM effect had used standardised NTM antigens in healthy subjects. RESULTS: In 24 studies involving 240,203 subjects BCG-vaccinated as infants, 20,406 (8.5%) had a TST of 10+ mm attributable to BCG, but only 56/5639 (1%) were TST-positive if tested > or =10 years after BCG. In 12 studies of 12,728 subjects vaccinated after their first birthday, 5314 (41.8%) had a false-positive TST of 10+ mm, and 191/898 (21.2%) after 10 years. Type of tuberculin test did not modify these results. In 18 studies involving 1,169,105 subjects, the absolute prevalence of false-positive TST from NTM cross-reactivity ranged from 0.1% to 2.3% in different regions. CONCLUSIONS: The effect on TST of BCG received in infancy is minimal, especially > or =10 years after vaccination. BCG received after infancy produces more frequent, more persistent and larger TST reactions. NTM is not a clinically important cause of false-positive TST, except in populations with a high prevalence of NTM sensitisation and a very low prevalence of TB infection.
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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 | 0.044 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".