The global dynamics of diabetes and tuberculosis: the impact of migration and policy implications
Bibliographic record
Abstract
The convergence between tuberculosis (TB) and diabetes mellitus (DM) will represent a major public health challenge in the near future. DM increases the risk of developing TB by two to three times and also increases the risk of TB treatment failure, relapse, and death. The global prevalence of DM is predicted to rise significantly in the next two decades, particularly in some of the low- and middle-income countries with the highest TB burden. Migration may add further complexity to the effort to control the impact on TB of the growing DM pandemic. Migration may increase the risk of DM, although the magnitude of this association varies according to country of origin and ethnic group, due to genetic factors and lifestyle differences. Migrants with TB may have an increased prevalence of DM compared to the native population, and the risk of TB among persons with DM may be higher in migrants than in autochthonous populations. Screening for DM among migrants, screening migrants with DM for active and latent TB, and improving access to DM care, could contribute to mitigate the effects of DM on TB.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".