A scoping review of interventions to mitigate common non-communicable diseases among people with TB
Bibliographic record
Abstract
BACKGROUND: Recommendations have been made to integrate screening for common non-communicable diseases (NCDs) within TB programs. However, we must ensure screening is tied to evidence-based interventions before scale-up. We aimed to map the existing evidence regarding interventions that address NCDs that most commonly affect people with TB.METHODS: We systematically searched PubMed, Medline, and Embase for studies that evaluated interventions to mitigate respiratory disease, cardiovascular disease, alcohol and substance use disorder, and mental health disorders among people with TB. We excluded studies that only screened for comorbidity but resulted in no further intervention. We also excluded studies focusing on smoking cessation interventions for which evidence-based guidelines are well established.RESULTS: The search identified 20 studies that met our inclusion criteria. The most commonly evaluated intervention was referral for diabetes care (6 studies). Other interventions included pulmonary rehabilitation (5 studies), care programs for alcohol use disorder (4 studies), and psychosocial support or individual counselling (5 studies).CONCLUSION: There is limited robust evidence to support identified interventions in changing individual outcomes, and a significant knowledge gap remains on the long-term durability of the interventions´ clinical benefit, reach, and effectiveness. Implementation research demonstrating feasibility and effectiveness is needed before scaling up.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".