Management of patients with multidrug-resistant/extensively drug-resistant tuberculosis in Europe: a TBNET consensus statement
Bibliographic record
Abstract
The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) substantially challenges TB control, especially in the European Region of the World Health Organization, where the highest prevalence of MDR/XDR cases is reported. The current management of patients with MDR/XDR-TB is extremely complex for medical, social and public health systems. The treatment with currently available anti-TB therapies to achieve relapse-free cure is long and undermined by a high frequency of adverse drug events, suboptimal treatment adherence, high costs and low treatment success rates. Availability of optimal management for patients with MDR/XDR-TB is limited even in the European Region. In the absence of a preventive vaccine, more effective diagnostic tools and novel therapeutic interventions the control of MDR/XDR-TB will be extremely difficult. Despite recent scientific advances in MDR/XDR-TB care, decisions for the management of patients with MDR/XDR-TB and their contacts often rely on expert opinions, rather than on clinical evidence. This document summarises the current knowledge on the prevention, diagnosis and treatment of adults and children with MDR/XDR-TB and their contacts, and provides expert consensus recommendations on questions where scientific evidence is still lacking.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".