Profile, Outcomes, and Determinants of Unsuccessful Tuberculosis Treatment Outcomes among HIV-Infected Tuberculosis Patients in a Nigerian State
Bibliographic record
Abstract
Background. Few studies have evaluated the rate of tuberculosis (TB)/human immunodeficiency virus (HIV) coinfection and the determinants of its treatment outcomes in Africa. We aimed to determine the predictors of unsuccessful treatment outcomes in HIV-infected tuberculosis patients in Nigeria. Methods. A retrospective cohort study design was used to assess adult TB/HIV patients who registered for TB treatment in two health facilities in Ebonyi State, Southeast Nigeria, between January 2011 and December 2012. Predictors of unsuccessful treatment outcomes were determined using multivariable logistic regression analysis. Results. Of 1668 TB patients, 342 (20.5%) were HIV coinfected. Of these, 195 (57%) had smear-negative pulmonary TB and 11 (3.2%) had extrapulmonary TB. Overall, 225 (65.8%) patients achieved successful outcomes, while 117 (34.2%) had unsuccessful outcomes. The unsuccessful treatment outcomes were due to "default" (9.9%), "death" (19%), "treatment failure" (1.5%), and "transferring out" (3.8%). Independent determinants for unsuccessful outcomes were receiving care at a public facility and noninitiation of antiretroviral therapy. Conclusion. There is need for the reevaluation of the quality of public sector treatment services provided for TB/HIV patients as well as further expansion of TB/HIV collaborative activities in rural areas, and interventions to reduce mortality and default rates among TB/HIV patients are urgently needed in Nigeria.
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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) | 0.000 | 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 | 0.001 |
| 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".