Tuberculosis Skin Test Screening in the National Tuberculosis Program of Trinidad and Tobago
Bibliographic record
Abstract
. About 5-10% of latent TB infections (LTBI) progress to active disease during the lifetime. Prevention of TB and treating LTBI is a critical component of the World Health Organization's (WHO) End TB Strategy. This study aims to examine the screening practices for prevention and treatment employed by the National Tuberculosis Program of Trinidad and Tobago in comparison to the WHO's standard guidelines. A cross-sectional retrospective study was conducted from the TB registers (2018-2019) for persons aged 18 years and above with recorded tuberculin skin test reactions (TST). Bivariate comparisons for categorical variables were made using Chi-square or Fisher's exact test. Binary logistic regression was used for exploring predictors of TST positivity with adjustment for demographic confounders in multivariable models. Of the total 1972 eligible entries studied, 384 (19.4%) individuals were tested positive with TST. TB contact screening (aOR 2.49; 95% CI 1.65, 3.75) and Bacillus Calmette-Guerin (BCG) vaccination status (aOR 1.66; 95% CI, 1.24 to 2.22) were associated with a positive TST reaction, whereas, preplacement screening failed to show such association when compared to those screened as suspect cases. The findings suggest that TB contact screening and positive BCG vaccination status are associated with TST positivity independent of age and gender.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".