An evaluation framework for new tests that predict progression from tuberculosis infection to clinical disease
Bibliographic record
Abstract
who have incipient disease and are likely to develop clinical tuberculosis (TB) in the near future to allow for targeted preventive treatment beyond the current risk groups. Recently, a target product profile was developed that outlines the minimal and optimal characteristics for such an incipient TB test. We describe an evaluation framework for generating evidence to inform the development of policy guidance for the use of such a new test by the World Health Organization. Two research objectives are addressed. 1) The predictive ability of an incipient TB test should be assessed in clinical evaluation studies that include the intended target population and follow-up of sufficient duration to observe whether individuals do or do not progress to clinical TB disease. 2) Studies are needed to evaluate the test under routine programmatic conditions and measure its impact on patient- or health-system-important outcomes. For both research objectives, study designs, methods and analysis are described, with the intent to inform the clinical development plans of test manufacturers, researchers and funders.
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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 | 0.316 | 0.235 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| 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".