Identifying the factors affecting quality of life among brucellosis patients in Herat, Afghanistan: a case-control study
Bibliographic record
Abstract
Background: Brucellosis remains a significant public health concern, especially in regions like the Mediterranean and Afghanistan. While its direct health effects are well-documented, its impact on quality of life is less explored.Objective: This study investigated the risk factors and quality of life effects of brucellosis in Herat, Afghanistan.Methods: Between October 2020 and February 2021, 155 participants were enrolled at Herat Regional Hospital and affiliated brucellosis laboratories. The sample included 75 individuals with confirmed brucellosis and 80 controls without a history of the disease. Data were collected using structured questionnaires, and quality of life was assessed with the SF-36. Statistical analyses included chi-square tests, logistic regression, and General Linear Model.Results: Key risk factors associated with brucellosis included marriage, lower education, contact with pets, infected family members, and using non-protected water (p < 0.05). Logistic regression confirmed increased odds of infection linked to these exposures. Quality of life assessments revealed significantly lower SF-36 scores among male patients across most components, with similar trends observed in female patients.Conclusion: Brucellosis significantly impacts quality of life, particularly among men, and is strongly linked to specific risk factors. Public health measures are essential to reduce infection rates and improve well-being in this region.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".