Getting the best possible evidence from observational studies of treatment of multidrug-resistant tuberculosis patients
Bibliographic record
Abstract
Background: The development of clinical recommendations by the World Health Organization (WHO) requires the systematic review of evidence and the grading of its quality. There is no high-quality evidence on treatment for multidrug-resistant tuberculosis (MDR-TB) patients using only standard second-line anti-TB drugs, as results of randomized controlled trials are not available. Well executed observational studies can meanwhile provide useful information that is applicable to clinical practice. For observational data to provide the best possible guidance, measures need to be taken to limit bias, imprecision, and heterogeneity. Intervention: WHO has established an expert Working Group to develop principles that national TB control programmes and other implementers could use to ensure the quality of observational study data. Results: Based on past experience, the group identified a number of critical points that need to be improved upon. Patient and programme-related data should be captured and reported in a standardized manner. These include bacteriological endpoints, proxies of disease severity, information on adverse drug reactions, the duration of current and past use of individual drugs, reason for change of regimen, use of accompanying medications, costs, hospitalization, patient support and type and extent of surgery. Conclusions: The Working Group recommends that programmes undertaking observational studies record most of these parameters, that they register data electronically, that they strive to publish their work, and that they share anonymous, individual patient data so as to update the knowledge base on MDR-TB patient outcomes.
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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 | 0.221 | 0.579 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".