The 2011 update of the World Health Organization guidelines for the programmatic management of drug-resistant tuberculosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it