Factors influencing drug-susceptible tuberculosis treatment outcomes in Romania and Ukraine
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) remains one of the most globally impactful infectious diseases, with a recorded mortality of 1.6 million in 2022. In Romania and Ukraine, two high burden countries in the context of the WHO European region, treatment is geared towards cure; however, this path is paved with significant challenges, from morbidity to loss to follow-up. METHODS: A retrospective study was performed for drug-susceptible TB patients hospitalised in three TB expertise centres in Romania and Ukraine using routinely collected data. Univariable and multivariable logistic regression analyses were used to assess predictors of three treatment outcomes: unfavourable outcomes, loss to follow-up, and death. RESULTS: A total of 838 patients diagnosed with drug-susceptible TB were included. Median hospitalisation was 39 days (IQR 25-67), and treatment duration was 7 months (IQR 6-8). Predictive variables differed by outcome. For unfavourable outcomes, the multivariable model included age > 65 years, chronic kidney disease, at least one cavity on chest X-ray, underweight status, and persistently abnormal laboratory parameters despite intervention. Independent predictors of loss to follow-up were alcohol use, COPD, TB infection within two years prior to admission, obesity, slow treatment response, and sputum microscopy ≥2 + . Predictors of death included age > 65 years, male sex, cirrhosis, chronic kidney disease, underweight status, persistently abnormal laboratory parameters, and slow treatment response. CONCLUSION: Contextualising factors influencing drug-susceptible TB treatment outcomes in different settings can support the development of tailored interventions that enable early identification of patients at higher risk, thereby avoiding unnecessary treatment effects.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".