Guidance for the Evaluation of Tuberculosis Diagnostics That Meet the World Health Organization (WHO) Target Product Profiles: An Introduction to WHO Process and Study Design Principles
Bibliographic record
Abstract
Existing high-priority target product profiles (TPPs) of the World Health Organization (WHO) establish important needs for tuberculosis (TB) diagnostic development. Building on this earlier work, this guidance series aims to provide study guidance for performing accuracy studies of novel diagnostic products that may meet the 4 high-priority WHO TPPs and thus enable adequate evidence generation to inform a WHO evidence review process. Diagnostic accuracy studies represent a fundamental step in the validation of all tests. Unfortunately, such studies often have limitations in design, execution, and reporting, leading to low certainty of the evidence about true test performance, which can delay or impede policy and scale-up decisions. This introductory paper outlines the following: (1) the purpose of this series of papers on study guidance; (2) WHO evidence needs and process for the development of policy guidelines for new TB diagnostic tests; and (3) study design considerations, ie, general diagnostic study considerations, intended use of test and role in the clinical pathway, choice of population and setting, index-test specific issues, suitable reference standard and comparators, study flow and specimen issues, and finally key issues beyond accuracy that should be considered. The other 4 papers in this series will provide more detailed guidance for each of the 4 WHO high-priority TPPs. By increasing the clarity around the clinical evaluation needs for tests that have the potential to meet the TPP specifications, we hope to support harmonized evidence generation and enable the WHO review process towards meeting the WHO End TB Strategy targets for reducing the incidence and mortality associated with TB.
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.
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 | 0.387 | 0.491 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.024 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".