Performance evaluation of the Molbio diagnostics Truenat MTB Ultima/COVID-19 multiplex assay for TB and COVID-19 case detection among people with symptoms suggestive of tuberculosis—a study protocol for clinical trials
Bibliographic record
Abstract
Background: Tuberculosis (TB) remains a major public health problem globally, as reflected in persistently high morbidity and mortality rates. Current control efforts have been further complicated by the ongoing SARS-CoV-2 (COVID-19) pandemic. Rapid molecular diagnostics remain crucial to identifying the millions of people with undiagnosed TB. Objectives: The study aimed to determine the diagnostic accuracy of Truenat MTB Ultima/COVID-19 for the detection of COVID-19 and TB among presumptive TB participants, using a microbiological reference standard (MRS) and a country-approved real-time reverse transcription polymerase chain reaction (RT-PCR) COVID-19 assay. Methods: This prospective cohort study assessed the diagnostic accuracy of the Truenat MTB Ultima/COVID-19 multiplex test among adults with presumptive TB, enrolled from healthcare facilities in four countries, aiming to reach 270 confirmed TB cases. The inclusion criteria were as follows: adults who self-reported at least one symptom suggestive of pulmonary TB, were willing to return for a day 2 visit, and agreed to provide oral swab samples for bio-banking. Patients were excluded from enrolment in the study if they had started anti-TB treatment within 60 days or TB preventive therapy within 6 months prior to enrolment or if they were unable to provide 3 mL of sputum or nasopharyngeal (NP) and tongue swab samples. Expected outcome: This study expected to obtain point estimates of the sensitivity and specificity of the Truenat MTB Ultima/COVID-19 multiplex test for TB and COVID-19 detection, compared to Xpert Ultra, among presumptive TB participants using an MRS and pre-defined COVID-19 assay.
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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 | 0.059 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".