Treatment Strategies and Challenges in the Co-Management of Type 2 Diabetes and Tuberculosis
Bibliographic record
Abstract
Despite rapid advances in the healthcare field, diabetes mellitus (DM) and tuberculosis (TB) continues to be a global burden that affects millions of people every year. The association between DM and TB has been known for an extended period. The last 15 years, however, have seen an increased number of studies showing that diabetes (both type 1 and type 2) increases the risk of tuberculosis because of impaired immune defences and likewise, TB may induce hyperglycemia and therefore increase the risk of DM. When DM and TB co-exist as dual diseases, it complicates management strategies as treatment outcomes are affected. In developing countries where the epidemic of DM and TB is rapidly growing, the presence of a concomitant disease becomes a challenge to the affected nation and could also impact DM and TB control on a global scale. This review brings together information on what is currently known about T2DM and TB as a double epidemic, the recommended treatment strategies, and the challenges involved in disease management. Furthermore, we address the future perspectives of the co-management of T2DM and TB and what can be done to overcome the shortcomings of currently available guidelines.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".