Factors Associated with Unsuccessful Outcomes of Tuberculosis Treatment in 125 Municipalities in Colombia 2014 to 2016
Bibliographic record
Abstract
Our aim was to identify the risk factors associated with unsuccessful outcomes of tuberculosis (TB) treatment in patients diagnosed between 2014 and 2016 in the 125 municipalities of Antioquia, Colombia. We studied a retrospective cohort of patients with TB diagnosed between 2014 and 2016, from national routine surveillance systems, in 125 municipalities of Antioquia. Factors associated with unsuccessful tuberculosis treatment outcomes (treatment failed, lost to follow up, or death) were identified utilizing a Poisson regression with robust variance. Over 3 years, of the 6,739 drug-susceptible tuberculosis patients, 73.4% had successful treatment and 26.6% unsuccessful outcomes (17% lost to follow up, 8.9% deaths, and 0.7% treatment failures). Patients with subsidized health insurance (Relative risk [RR]: 2.4; 95% CI: 2.1-2.8) and without health insurance (RR: 2.5; 95% CI: 2.1-3.0) had a higher risk for unsuccessful tuberculosis treatment compared to those with contributive health insurance. Other risk factors included age over 15 years, male sex, homelessness, people living with HIV, previous treatment, and primary diagnosis during hospitalization. Protective factors were living in a rural area and extrapulmonary disease. It is important to generate strategies that improves tuberculosis diagnosis in primary healthcare institutions. In addition, it is imperative to initiate new research about the barriers and obstacles related to patients, healthcare workers and services, and the health system, including the analysis of urban violence, to understand why the goal of TB treatment success has not been reached.
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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 | 0.003 |
| 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 | 0.001 | 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".