The epidemiologic relationship between tuberculosis and non-tuberculous mycobacterial disease: a systematic review
Bibliographic record
Abstract
SETTING: Tuberculosis (TB) rates are decreasing in many areas, while non-tuberculous mycobacteria (NTM) infection rates are increasing. The relationship between the epidemiology of TB and NTM infections is not well understood. OBJECTIVE: To understand the epidemiologic relationship between TB and NTM disease worldwide. DESIGN: A systematic review of Medline (1946-2014) was conducted to identify studies that reported temporal trends in NTM disease incidence. TB rates for each geographic area included were then retrieved. Linear regression models were fitted to calculate slopes describing changes over time. RESULTS: There were 22 studies reporting trends in rates of NTM disease, representing 16 geographic areas over four continents: 75% of areas had climbing incidence rates, while 12.5% had stable rates and 12.5% had declining rates. Most studies (81%) showed declining TB incidence rates. The proportion of incident mycobacterial disease caused by NTM was shown to be rising in almost every geographic area (94%). CONCLUSION: We found an increase in the proportion of mycobacterial disease caused by NTM in many parts of the world due to a simultaneous reduction in TB and increase in NTM disease. Research into the interaction between mycobacterial infections may help explain this inverse relationship.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".