Potential role for interferon-γ release assays in tuberculosis screening in a remote Canadian community: a case series
Bibliographic record
Abstract
BACKGROUND: Current Canadian guidelines suggest that neonatal Bacille Calmette-Guérin (BCG) vaccination does not result in false-positive tuberculosis (TB) skin tests, despite a growing body of evidence that interferon-γ release assays may be a more specific alternative in identifying latent tuberculosis infections in vaccinated populations. We set out to evaluate the relationship between TB skin tests and interferon-γ release assays in patients who previously received neonatal BCG vaccine. METHODS: All children with a positive skin test at age 14 years in a remote community north of Sioux Lookout, Ontario, were considered for interferon-γ release assay testing. RESULTS: Of the 11 children who underwent routine screening at 14 years of age for latent TB infection, 7 had a positive TB skin test (≥ 10 mm). All 7 of these children had received the BCG vaccine as newborns and all had a negative TB skin test during their routine screening at 4 years of age. No potential exposure to active TB could be identified. Chest radiographs were normal, and none of the children had symptoms suggestive of active TB. The 7 children underwent interferon-γ release assay testing using QuantiFERON Gold. All 7 tests were negative. INTERPRETATION: With the addition of interferon-γ release assays to routine skin test screening, we provide evidence that neonatal BCG vaccination may contribute to a false-positive skin test in youth at 14 years of age. Consideration should be given to the possibility that neonatal BCG may contribute to false-positive TB skin tests.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".