Establishing a cohort in a developing country: Experiences of the diabetes-tuberculosis treatment outcome cohort study
Bibliographic record
Abstract
BACKGROUND: Prospective cohort studies are instrumental in generating valid scientific evidence based on identifying temporal associations between cause and effect. Researchers in a developing country like Pakistan seldom undertake cohort studies hence little is known about the challenges encountered while conducting them. We describe the retention rates among tuberculosis patients with and without diabetes, look at factors associated with loss to follow up among the cohort and assess operational factors that contributed to retention of cohort. METHODS: A prospective cohort study was initiated in October 2013 at the Gulab Devi Chest Hospital, Lahore, Pakistan. We recruited 614 new adult cases of pulmonary tuberculosis, whose diabetic status was ascertained by conducting random and fasting blood glucose tests. The cohort was followed up at the 2nd, 5th and 6th month while on anti-tuberculosis therapy (ATT) and 6months after ATT completion to determine treatment outcomes among the two groups i.e. patients with diabetes and patients without diabetes. RESULTS: The overall retention rate was 81.9% (n=503), with 82.3% (93/113) among patients with diabetes and 81.8% (410/501) among patients without diabetes (p=0.91). Age (p=0.001), area of residence (p=0.029), marital status (p=0.001), educational qualification (p=<0.001) and smoking (p=0.026) were significantly associated with loss to follow up. Respondents were lost to follow up due to inability of research team to contact them as either contact numbers provided were incorrect or switched off (44/111, 39.6%). CONCLUSION: We were able to retain 81.9% of PTB patients in the diabetes tuberculosis treatment outcome (DITTO) study for 12months. Retention rates among people with and without diabetes were similar. Older age, rural residence, illiteracy and smoking were associated with loss to follow up. The study employed gender matched data collectors, had a 24-h helpline for patients and sent follow up reminders through telephone calls rather than short messaging service, which might have contributed to retention of cohort.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".