Diagnostic Performance of the Pluslife MiniDock MTB and Molbio MTB Ultima Assays to Detect Tuberculosis From Tongue and Sputum Swabs Among Outpatients and in Active Case Finding in Cameroon
Bibliographic record
Abstract
BACKGROUND: Cost and infrastructure requirements limit access to current rapid molecular diagnostic testing for tuberculosis (TB). Recent developments in novel, swab-based assays that can be used closer to the point of care offer the potential to change the TB diagnostic landscape. METHODS: We assessed the diagnostic accuracy of 2 tests, Molbio Truenat MTB Ultima (MTB Ultima) and Pluslife MiniDock MTB Test (MiniDock MTB), among consecutively enrolled individuals aged 15 years and older, against the reference standard of culture and comparators of microscopy and Xpert MTB/RIF Ultra. Two tongue swabs, either self-collected or healthcare worker collected, and 2 sputum specimens were requested from each participant; MiniDock MTB was tested the same day and MTB Ultima was tested after storage. RESULTS: From February to June 2025, 1097 participants were enrolled in communities (382) and at health facilities (715). Sensitivities of sputum and tongue swabs on MiniDock MTB among 132 people with culture-positive TB were 86% (95% confidence interval [CI], 79-91) and 76% (95% CI, 68-82), respectively, and 67% (95% CI, 51-79) and 44% (29-59) among those with smear-negative TB. Sensitivities of sputum and tongue swabs on MTB Ultima were 84% (97/116; 95% CI, 76-89) and 74% (67/91; 95% CI, 64-82), respectively, and 58% (95% CI, 41-74) and 33% (95% CI, 18-53) among those with smear-negative TB. CONCLUSIONS: In this population, the performance of both MiniDock MTB and MTB Ultima on tongue and sputum swabs was similar to target product profile thresholds for near point-of-care TB tests. Further studies to evaluate performance in diverse populations and settings are needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".