Epidemiological trends of tuberculosis in Abbottabad and Mansehra, northern Pakistan: a retrospective study (2015–2022)
Bibliographic record
Abstract
Pakistan is the fifth most heavily tuberculosis (TB) afflicted country in the world. This study aimed to investigate the epidemiological characteristics of TB in Abbottabad and Mansehra districts mainly because these populations remained unexplored previously. This was a retrospective study carried out between June 2022 and August 2022. Data on TB patients ranging in duration from 1st quarter of 2015 to 1st quarter of 2022 were obtained from tertiary care hospitals and were analyzed using GraphPad Prism v.8.0.1. Data from a total of 16,140 TB patients from both populations were included. Among the patients, ~ 64% had pulmonary TB, and the remaining 36% had extrapulmonary TB. The difference in the TB incidence rate between male and female patients was not significant (50.3% vs. 49.7%, p = 0.0545). The TB incidence rate gradually increased with age, with the highest incidence rate observed in late adolescence and early adulthood. Overall, we noticed a greater proportion of clinically diagnosed TB patients than of those diagnosed through bacteriological or histopathological testing (64% vs. 36%, respectively, p < 0.0001). The proportion of patients who experienced TB relapse was significantly lower than that of patients who experienced new disease (6.8% vs. 93.2%, respectively; p < 0.0001). More than 90% of the patients who were initially diagnosed in hospitals successfully completed their treatment. This first epidemiological study of TB in Abbottabad and Mansehra will provide a baseline for future research in the region. Based on our findings, we urge healthcare professionals and relevant stakeholders to prioritize a strategy that can ensure data integrity, enhance diagnostic accuracy, perform age-targeted screening, and sustain effective treatment protocols to combat TB locally and nationally.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".