Immunodiagnosis of Tuberculosis: State of the Art
Bibliographic record
Abstract
Undiagnosed and mismanaged tuberculosis (TB) continues to fuel the global TB epidemic. Rapid, accurate and early diagnosis of TB is therefore a priority to improve TB case detection and interrupt transmission. Although considerable improvements have been made in TB diagnostics, there are two major gaps in the existing diagnostics pipeline: (1) lack of a simple accurate point-of-care test that can be used for rapid diagnosis at the primary care level; (2) lack of a biomarker (or combination of biomarkers) that can be used to identify latently infected individuals who will benefit most from preventive therapy. Currently available commercial serological (antibody detection) tests are inaccurate and do not improve patient outcomes. Despite this evidence, dozens of serological tests are sold and used in countries (e.g. India) with weak regulatory systems, especially in the private sector. Recognizing the threat posed by these suboptimal tests, a World Health Organization (WHO) Expert Group has strongly recommended against the use of serological tests for the diagnosis of pulmonary and extra-pulmonary TB. Another WHO Expert Group has discouraged the use of interferon-γ release assays for active pulmonary TB diagnosis in low- and middle-income countries. All existing tests for latent TB infection appear to have only modest predictive value and further research is needed to identify highly predictive biomarkers.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".