Investigating the Prevalence of Latent Tuberculosis among Healthcare Workers of Major Hospitals of Ahvaz, Iran
Bibliographic record
Abstract
Background and Objective: Healthcare and laboratory workers in hospitals have a higher exposure to hospital-acquired infections (HAIs) than the general populations. Tuberculosis (TB) infection is a common HAI that is communicated from the patients with TB admitted or hospitalized in the healthcare centres. This study aims to determinate the incidence and prevalence of latent TB infection among healthcare workers in the major Hospitals in Ahvaz, Khuzestan province, Iran. Methods: This is a case-control study conducted on healthcare workers diagnosed with TB between January 2010 to December 2017. The study population consisted of healthcare workers of the three major hospitals of Ahvaz, Khuzestan including Imam Khomeini Hospital, Golestan Hospital and Shafa Hospital. All subjects underwent Mantoux tuberculin skin test (TST) and after 48 to 72 hours the reaction was measured as the length of induration. The induration equal to or greater than 10 mm was considered as positive TB. Moreover, the subjects and controls were asked to complete a self-administered questionnaire on potential risk factors for TB. Results: Among 513 subjects, male and female subjects were respectively 186 and 327. Of all subjects, 67 subjects (male: 42; female: 25) showed positive TST (10 mm ≤ induration) and 275 subjects showed 5 mm >induration. There was a significant relationship between TST result and the workplace of the subjects. Gender showed no significant relationship with the TST result. Conclusion: All healthcare workers who are in direct contact with TB patients must undergo regular TB screening test and the workers should be trained for self-conducting TST.
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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 | 0.001 |
| 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 | 0.000 | 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".