Prevalence of LTBI using IGRA and TST in a cohort of health professional trainees from India
Bibliographic record
Abstract
Objectives: Estimate the prevalence of LTBI among health professional trainees at a referral hospital in India, using Tuberculin skin test (TST) and Quantiferon TB Gold In-tube, (QFT). Methods: From November 2009 to February 2011, students in health professional programs (except medical and nursing students) were approached for consent. In addition to a detailed questionnaire on TB exposure, participants underwent TST (10 mm) and the QFT-GIT (0.35 IU/ml). Results: 164 students completed testing. Mean age was 21.5 yrs, 48.8% were female and 59.15% had BCG. Mean time in health care was 11.5 months, and 21.6% recalled contact with PTB cases. Seventy-nine (48.2%, 95%CI: 40.3-56.1%) were positive by TST, and 38 (23.2%, 95% CI: 16.9-30.4%) were positive by QFT. In a cohort of nursing students from the same institution, prevalence was estimated at 40.3% (TST) and 17.04% (QFT), thus lower than the health professional cohort. Possible explanations include a higher proportion of male students compared with nursing, also present cohort were less likely to own a car or house as compared with nursing students, suggesting lower SES. Multivariate logistic regression showed age was associated with QFT positivity but not TST (OR=1.24, 95%CI: 1.01-1.52). Length of time in health care, family income, direct contact with TB all showed no association with either test. Participating in sputum collection and/or processing showed a trend towards QFT positivity (but not TST positivity), although this did not reach statistical significance (OR=1.1 (95%CI: 0.89-1.4). Conclusions: LTBI is common among health professional trainees in India, however, risk factors appear to be better correlated with QFT.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".