The Feasibility of the Interferon Gamma Release Assay and Predictors of Discordance with the Tuberculin Skin Test for the Diagnosis of Latent Tuberculosis Infection in a Remote Aboriginal Community
Bibliographic record
Abstract
BACKGROUND: The tuberculin skin test (TST) is the standard test used to screen for latent TB infection (LTBI) in the northern Canadian territory of Nunavut. Interferon gamma release assays (IGRA) are T cell blood-based assays to diagnose LTBI. The Bacillus Calmette-Guerin (BCG) vaccine is part of the routine immunization schedule in Nunavut. The objective of this study was to test the feasibility, and predictors of discordance between the Tuberculin Skin Test (TST) and the IGRA assay in a medically under-serviced remote arctic Aboriginal population. METHODS: Both the TST and QuantiFERON-TB Gold (Qiagen group) IGRA tests were offered to people in their homes as part of a public health campaign aimed at high TB risk residential areas in Iqaluit, Nunavut, Canada. Feasibility was measured by the capacity of the staff to do the test successfully as measured by the proportion of results obtained. RESULTS: In this population of predominantly young Inuit who were mostly BCG vaccinated, the use of IGRA for the diagnosis of LTBI was feasible. IGRA testing resulted in more available test results reaching patients (95.6% vs 90.9% p = 0.02) but took longer (median 8 days (IGRA) vs 2 days (TST), p value < 0.0001). 44/256 participants (17.2%) had discordant results. Multivariable regression analysis suggested that discordant results were most likely to have received multiple BCG vaccinations (RR 20.03, 95% CI, 3.94-101.82)), followed by BCG given post infancy (RR 8.13, 95% CI, 2.54-26.03)) and then to a lesser degree when BCG was given in infancy (RR 6.43, 95% CI, 1.72-24.85). INTERPRETATION: IGRA is feasible in Iqaluit, Nunavut, a remote Arctic community. IGRA testing results in more test results available to patients compared to TST. This test could result in fewer patients requiring latent TB treatment among those previously vaccinated with BCG in a region with limited public health human resources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".