Rifampicin-resistant Tuberculosis and Associated Factors Among Pulmonary Tuberculosis Patients in Mahakali Provincial Hospital, Nepal
Bibliographic record
Abstract
The study aims at assessing the prevalence of RR-TB and identify associated factors among pulmonary TB patients using GeneXpert MTB/RIF assay data from January to December 2023. Tuberculosis (TB) continues to pose a significant global health threat, with rifampicin-resistant (RR) strains presenting a formidable challenge to disease control efforts. This study based the retrospective cross-sectional study conducted at Mahakali Provincial Hospital in Nepal. Out of 2587 presumptive TB cases, 11.8% were confirmed positive for TB, with males constituting a significantly higher proportion (66.1%) than females (33.9%). Among TB-positive cases, 3% showed resistance to rifampicin, predominantly affecting males (77.8%). Age group analysis revealed higher TB detection rates in the 46-60 years group, while rifampin -resistance tuberculosis (RR-TB) cases were more evenly distributed across age groups without statistical significance. Ethnicity and residential locality did not show significant associations with RR-TB. Multivariate logistic regression highlighted gender as RR-TB and associated factors among pulmonary tuberculosis patients in a Mahakali Provincial Hospital, Nepal. Rifampicin-resistant TB (RR-TB) remains a significant challenge at Mahakali Provincial Hospital, with a 3% prevalence, predominantly affecting males. Gender is a key factor in RR-TB prevalence. To manage and reduce RR-TB effectively, it is necessary to implement targeted interventions for high-risk groups, particularly males, and to enhance diagnostic capabilities.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".