Framework for the evaluation of new tests for tuberculosis infection
Bibliographic record
Abstract
The scale-up of tuberculosis (TB) preventive treatment (TPT) must be accelerated to achieve the targets set by the United Nations High-level Meeting on TB and the End TB Strategy. The scale-up of effective TPT is hampered by concerns about operational challenges to implement the existing tests for TB infection. New simpler tests could facilitate the scale-up of testing for TB infection. We present a framework for evaluation of new immunodiagnostic tests for the detection of TB infection, with an aim to facilitate their standardised evaluation and accelerate adoption into global and national policies and subsequent scale-up. The framework describes the principles to be considered when evaluating new tests for TB infection and provides guidance to manufacturers, researchers, regulators and other users on study designs, populations, reference standards, sample size calculation and data analysis and it is also aligned with the Global Strategy for TB Research and Innovation adopted by the World Health Assembly in 2020. In addition, we briefly describe technical issues that should be considered when evaluating new tests, including the safety for skin tests, costs incurred by patients and the health system, and operational characteristics.
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 | 0.360 | 0.232 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".