Guidance for Studies Evaluating the Accuracy of Sputum-Based Tests to Diagnose Tuberculosis
Bibliographic record
Abstract
Tests that can replace sputum smear microscopy have been identified as a top priority diagnostic need for tuberculosis by the World Health Organization. High-quality evidence on diagnostic accuracy for tests that may meet this need is an essential requirement to inform decisions about policy and scale-up. However, test accuracy studies are often of low and inconsistent quality and poorly reported, leading to uncertainty about true test performance. Here we provide guidance for the design of diagnostic test accuracy studies of sputum smear-replacement tests. Such studies should have a cross-sectional or cohort design, enrolling either a consecutive series or a random sample of patients who require evaluation for tuberculosis. Adults with respiratory symptoms are the target population. The reference standard should at a minimum be a single, automated, liquid culture, but additional cultures, follow-up, clinical case definition, and specific measures to understand discordant results should also be included. Inclusion of smear microscopy and Xpert MTB/RIF (or MTB/RIF Ultra) as comparators is critical to allow broader comparability and generalizability of results, because disease spectrum can vary between studies and affects relative test performance. Given the complex nature of sputum (the primary specimen type used for pulmonary TB), careful design and reporting of the specimen flow is essential. Test characteristics other than accuracy (such as feasibility, implementation considerations, and data on impact on patient, population and health systems outcomes) are also important aspects.
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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 | 0.168 | 0.477 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.017 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.014 | 0.005 |
| Research integrity | 0.032 | 0.012 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".