Risk of adverse treatment outcomes among new pulmonary TB patients co-infected with diabetes in Pakistan: A prospective cohort study
Bibliographic record
Abstract
PURPOSE: The escalating burden of diabetes in countries tackling high burden of tuberculosis (TB) has adverse implications for co-infected individuals and National TB control efforts. We aimed to study whether there was a difference in treatment outcome among diabetic and non-diabetic pulmonary TB patients and identify the determinants of treatment outcome among the two groups. MATERIALS AND METHODS: This prospective cohort study recruited new patients of pulmonary tuberculosis (PTB) aged 15 years and above who were diagnosed at and registered with Gulab Devi Chest Hospital, Lahore, Pakistan for anti-tuberculosis treatment (ATT). PTB patients were screened for diabetes using random and fasting blood glucose tests. Diabetic and non-diabetic PTB patients were followed up at second, fifth and sixth month of ATT and 6 months after ATT completion to determine treatment outcome. Multivariate logistic regression analysis was conducted to assess association between various factors and treatment outcome. RESULTS: Of 614 PTB patients, (n = 113 [18%]) were diabetic and (n = 501 [82%]) non-diabetic. Final model showed that diabetics were more likely to experience an unfavorable outcome as compared to non-diabetics (adjusted odds ratio [aOR] = 2.70, 95% Confidence Interval [CI] = 1.30 to 5.59). Other predictors of unfavorable outcome included rural residence (aOR = 1.98, 95% CI = 1.14 to 3.47), body mass index less than 18.50 (aOR = 1.89, 95% CI = 1.03 to 3.47) and being a smoker (aOR = 2.03, 95%CI = 1.04 to 3.94). CONCLUSION: Our study shows unfavorable treatment outcome among diabetic PTB patients. Integrated models of care with screening/testing and management for diabetes and TB could improve TB treatment outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".