Exploring Sex Differences in Risk Factors and Quality of Life Among Tuberculosis Patients in Herat, Afghanistan: A Case-Control Study
Bibliographic record
Abstract
Objectives: Tuberculosis (TB) is a significant public health concern in Afghanistan, with a high burden of disease in the western province of Herat. This study explored the risk factors of TB and TB’s impact on the quality of life of patients in Herat. Methods: A total of 422 TB patients and 514 controls were recruited at Herat Regional Hospital and relevant TB laboratories between October 2020 and February 2021. Data was collected through interviews using a structured questionnaire and the SF-36 questionnaire. Descriptive statistics, chi-square tests, Multivariate General Linear Model, and logistic regression analysis were used to analyze the data. Results: The results showed that male sex (p = 0.023), chronic disease (p = 0.038), lower education levels (p < 0.001), and worse health status (p < 0.001) were significantly associated with higher odds of TB infection. The study also found that TB patients had significantly lower quality of life scores in almost all components (p < 0.05). Conclusion: This study provides important insights into the specific ways in which TB affects the wellbeing of patients in Afghanistan. The findings highlight the importance of addressing the psychological and social dimensions of TB.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".