Predictors of Positive Tuberculin Skin Test (TST) Results after 2‐Step TST among Health Care Workers in Manitoba, Canada
Bibliographic record
Abstract
BACKGROUND: Baseline 2-step tuberculin skin testing (TST) is recommended for health care workers (HCWs) to identify cases of the "boosting phenomenon" (i.e., a negative initial TST result followed by a positive result) and to track the risk of acquiring occupational tuberculosis. However, the 2-step TST has been shown to be insufficient to identify all cases of the booster phenomenon in older adults and refugees. The objective of this study was to identify whether a history of bacille Calmette-Guérin (BCG) vaccination and foreign birth--variables that are known to be associated with the booster phenomenon--remain predictors of a positive TST result in a group of HCWs documented to have negative 2-step TST results (i.e., 2 TSTs done 7-28 days apart with indurations <10 mm in diameter). METHODS: We performed a retrospective analysis of an employee database in a tertiary care hospital in Winnipeg, Canada. The study population was comprised of 698 HCWs with negative 2-step TST results who underwent a TST 0-2 years after completion of the 2-step procedure. RESULTS: Forty-six HCWs (6.6%) had a positive TST result 0-2 years after the 2-step test. In a multiple logistic regression analysis controlling for age, BCG vaccination, foreign birth, sex, and work setting, only history of BCG vaccination (odds ratio [OR], 8.38; 95% confidence interval [CI], 4.04-17.4), foreign birth (OR, 3.19; 95% CI, 1.53-6.62), and high-risk work setting (OR, 2.93; 95% CI, 1.44-5.95) were associated with a positive TST result. CONCLUSIONS: Even for HCWs with negative results of 2-step TST, foreign birth and history of BCG vaccination are associated with a positive result of a future TST. Some positive TST results in such HCWs are related to nonoccupational factors, including delayed boosting, rather than to conversion due to recent tuberculosis contact.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".