The end of the road for blood RNA biomarkers as triage tests for symptomatic pulmonary tuberculosis among spontaneous sputum producers?
Bibliographic record
Abstract
Extract In 2022, an estimated 3.1 million people with tuberculosis (TB) remained undiagnosed [1] despite the global roll-out of rapid molecular tests for Mycobacterium tuberculosis, such as Xpert Ultra and Truenat MTB plus [2]. In pulmonary TB, these tests rely on the availability of a respiratory sample. Sputum induction or invasive sampling are required in sputum-scarce individuals, but often unavailable in resource-limited settings. Moreover, even among spontaneous sputum producers, resource constraints may limit the number of rapid molecular tests for M. tuberculosis that can be performed programmatically. Detecting host immune perturbations in response to M. tuberculosis infection, for example with blood RNA biomarkers, has been proposed as a triage approach to guide further confirmatory testing. Such an approach seeks to reduce the number of confirmatory tests performed, and direct these tests to higher risk individuals. Numerous RNA signatures, comprising quantitation of the expression of one or more genes, have been described with excellent diagnostic performance in their discovery populations.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.024 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".