Global Trends in Resistance to Antituberculosis Drugs
Bibliographic record
Abstract
BACKGROUND: Data on global trends in resistance to antituberculosis drugs are lacking. METHODS: We expanded the survey conducted by the World Health Organization and the International Union against Tuberculosis and Lung Disease to assess trends in resistance to antituberculosis drugs in countries on six continents. We obtained data using standard protocols from ongoing surveillance or from surveys of representative samples of all patients with tuberculosis. The standard sampling techniques distinguished between new and previously treated patients, and laboratory performance was checked by means of an international program of quality assurance. RESULTS: Between 1996 and 1999, patients in 58 geographic sites were surveyed; 28 sites provided data for at least two years. For patients with newly diagnosed tuberculosis, the frequency of resistance to at least one antituberculosis drug ranged from 1.7 percent in Uruguay to 36.9 percent in Estonia (median, 10.7 percent). The prevalence increased in Estonia, from 28.2 percent in 1994 to 36.9 percent in 1998 (P=0.01), and in Denmark, from 9.9 percent in 1995 to 13.1 percent in 1998 (P=0.04). The median prevalence of multidrug resistance among new cases of tuberculosis was only 1.0 percent, but the prevalence was much higherin Estonia (14.1 percent), Henan Province in China (10.8 percent), Latvia (9.0 percent), the Russian oblasts of Ivanovo (9.0 percent) and Tomsk (6.5 percent), Iran (5.0 percent), and Zhejiang Province in China (4.5 percent). There were significant decreases in multidrug resistance in France and the United States. In Estonia, the prevalence in all cases increased from 11.7 percent in 1994 to 18.1 percent in 1998 (P<0.001). CONCLUSIONS: Multidrug-resistant tuberculosis continues to be a serious problem, particularly among some countries of eastern Europe. Our survey also identified areas with a high prevalence of multidrug-resistant tuberculosis in such countries as China and Iran.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".