Interferon-gamma release assays for latent tuberculosis infection screening in Canadian federal correctional facilities
Bibliographic record
Abstract
BACKGROUND: The correctional setting presents an opportunity for latent TB infection (LTBI) screening in an otherwise difficult to reach demographic. We evaluate factors associated with the fidelity of the tuberculin skin test (TST) and interferon-gamma release assay (IGRA), specifically the QuantiFERON®-TB Gold In-Tube assay (QFT-GIT), explain factors associated with discordance, and report LTBI treatment outcomes.METHODS: We describe the association between demographic and clinical variables, and predictors of concordance with IGRA using univariate logistic regression in a population of TST-positive inmates. We report outcomes among those offered LTBI treatment.RESULTS: We observed concordance between TST and QFT-GIT in 90 of 306 (29.4%) inmates. Persons with TST+/QFT-GIT+ results were less likely to be male (OR 3.94, 95% CI 1.73–8.97) or have a BCG vaccination history (OR 0.34, 95% CI 0.12–0.95), and more likely to be foreign-born (P < 0.001). Of the 108 inmates offered LTBI treatment, 65 (60.1%) accepted and 51 (78.0%) completed. TST/QFT-GIT discordance has not been associated with disease during follow-up.CONCLUSION: Our findings suggest that TST/QFT-GIT discordance in Canadian federal inmates is common; however, low-risk of disease progression in those with discordance suggests that a shift towards IGRA-based screening is warranted and feasible.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".