Drug resistance and epidemiology characteristics of multidrug-resistant tuberculosis patients in 17 provinces of China
Bibliographic record
Abstract
As China is one of high MDR-TB burden countries, it is important to determine the drug resistant pattern and clinical characteristics of multidrug resistant tuberculosis (MDR-TB). We conducted a comprehensive and nationwide study on MDR-TB in 17 provinces for the period from June 2009 to June 2015, and a total of 1154 cases of MDR-TB were finally investigated. The study sought to assess the clinical features and contrast drug susceptibility profiles of MDR-TB patients in China. Cavitary disease, young age, and long duration of TB disease among MDR-TB patients were important predictors. A high resistance proportion of first-line drugs was observed in Beijing, Shanghai and Tianjin. Resistant proportions of second-line anti-TB drugs in western region for amikacin, aminosalicylic acid, and levofloxacin were higher than eastern and central regions. High levels of drug resistance were seen in earlier cases (before 2011) and outpatients. We found high levels of resistance to 1st- and 2nd-line drugs in all settings, with considerable variabilities in terms of different Directly Observed Treatment Short Course (DOTS) programme, level of economic development(eastern, central and western regions) and patient source (inpatients and outpatients). Timely drug susceptibility testing (DST) and effective management are necessary to ensure an early detection of MDR-TB and its proper treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".