Prevalence of tuberculosis infection in immunological-competent prison inmates using Interferon Gamma Release Assay (IGRA) in a central jail of Assam, India
Bibliographic record
Abstract
Abstract Tuberculosis, the leading cause of death among infectious origin, globally causes disease to more than 10 million people each year. Available estimates indicate a higher prevalence amongst vulnerable populations. Aim and objectives To estimate the prevalence of tuberculosis infection among immunological-competent prison inmates using Interferon Gamma Release Assay (IGRA) in a Central Jail of Assam, India, and to assess knowledge and practice related to tuberculosis infection, mode of transmission, and measures to prevent it. Methods An analytical cross-sectional study was conducted among immunological-competent-prison inmates of a Central Jail of Assam. The sample size was 220; calculated using nMaster software. Socio-demographic, environmental, anthropometric, behavioral, dietary, and knowledge were assessed. Interferon Gamma Release Assay test to detect Tuberculosis infection was done and all symptomatic were tested by NAAT (Nucleic Acid Amplification Test). Statistical analysis included univariate analysis, with Chi-square tests (or Fischer's Exact Test for small sample sizes) used to assess the association between categorical variables. Results Tuberculosis infection was found in 24.2 %, while 71.2 % were negative and 4.6 % were indeterminate. History of Tuberculosis was present in 10.8 % (24) of which 37.5 % (9) had pulmonary Tuberculosis. Behavioral risk factors like current smoking were found among 33.8 % (75) while alcohol consumption was 63.1 % (140). Co-morbidity like diabetes [7 (3.2 %)], HIV/AIDs [1.4 % (3)], hepatitis B along with HIV [1 (0.5 %)] was present. Most participants [178 (80.2 %)] had ever heard of tuberculosis, while 53.6 % did not know how TB is transmitted. Anthropometric examination revealed the average body mass index as 21.82 ± 3.87 and the majority were on deficient diet. Conclusion Ongoing tuberculosis infection among prison inmate suggest implementation of different preventive strategies in prison like increasing awareness programs, regular screening, dietary improvement, environmental measures and better health care service provisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".