The 2011 update of the World Health Organization guidelines for the programmatic management of drug-resistant tuberculosis
Bibliographic record
Abstract
Introduction: The production of guidelines for the programmatic management of drug-resistant tuberculosis fit into the mandate of the World Health Organization (WHO) to provide technical support to countries to reinforce care of drug resistant tuberculosis patients. Methods: WHO commissioned systematic reviews of evidence, including meta-analysis and modeling studies, to summarize evidence on priority questions regarding case finding, treatment regimens for multidrug-resistant TB (MDR-TB), monitoring of response to MDR-TB treatment and models of care. The quality of evidence assembled varied from low to very low. A multidisciplinary expert panel used the GRADE approach to develop recommendations based on best available evidence. Findings: The recommendations encourage the wider use of rapid drug-susceptibility testing with molecular techniques to detect rifampicin resistance and treat patients adequately. The use of culture remains important for the early detection of failure during MDR-TB treatment. The guidelines provide recommendations about the early use of anti-retroviral agents for TB patients with HIV who are on second-line TB drug regimens. Systems that primarily employ ambulatory models of care to manage MDR-TB patients are recommended over others based mainly on hospitalization. Conclusion: Practitioners and decision makers involved in MDR-TB care should be guided in their work by these updated recommendations. Additional research is necessary to improve the quality of existent evidence, particularly on regimen composition and duration of 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 | 0.037 | 0.082 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".