New Diagnostics for Latent and Active Tuberculosis: State of the Art and Future Prospects
Bibliographic record
Abstract
Tuberculosis (TB) continues to be the world's most important infectious cause of morbidity and mortality among adults. Nearly 9 million people develop TB disease each year, and an estimated 1.6 million die from the disease. Despite this enormous global burden, case detection rates are low, posing serious hurdles for TB control. Conventional TB diagnosis continues to rely on antiquated tests such as sputum smear microscopy, culture, tuberculin skin test, and chest radiography. These tests have several limitations and perform poorly in populations affected by the HIV epidemic. Conventional tests for detection of drug resistance are time consuming, tedious, and inaccessible in most settings. In this review, we describe recent advances in the diagnosis of latent and active TB, and detection of drug resistance. Although the perfect test will not be ready for large-scale roll-out and integration into routine TB care services for some time, substantial progress has been made in expanding the TB diagnostic product pipeline. With the resurgence of interest in the development of new tools for TB control, and the recent influx of funding and political support, it is likely that the next few years will see the introduction of new diagnostic tools into routine TB control programs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".