Evaluating the Auto-MODS Assay, a Novel Tool for Tuberculosis Diagnosis for Use in Resource-Limited Settings
Bibliographic record
Abstract
There is an urgent need for simple, rapid, and affordable diagnostic tests for tuberculosis (TB) to combat the great burden of the disease in developing countries. The microscopic observation drug susceptibility assay (MODS) is a promising tool to fill this need, but it is not widely used due to concerns regarding its biosafety and efficiency. This study evaluated the automated MODS (Auto-MODS), which operates on principles similar to those of MODS but with several key modifications, making it an appealing alternative to MODS in resource-limited settings. In the operational setting of Chiang Rai, Thailand, we compared the performance of Auto-MODS with the gold standard liquid culture method in Thailand, mycobacterial growth indicator tube (MGIT) 960 plus the SD Bioline TB Ag MPT64 test, in terms of accuracy and efficiency in differentiating TB and non-TB samples as well as distinguishing TB and multidrug-resistant (MDR) TB samples. Sputum samples from clinically diagnosed TB and non-TB subjects across 17 hospitals in Chiang Rai were consecutively collected from May 2011 to September 2012. A total of 360 samples were available for evaluation, of which 221 (61.4%) were positive and 139 (38.6%) were negative for mycobacterial cultures according to MGIT 960. Of the 221 true-positive samples, Auto-MODS identified 212 as positive and 9 as negative (sensitivity, 95.9%; 95% confidence interval [CI], 92.4% to 98.1%). Of the 139 true-negative samples, Auto-MODS identified 135 as negative and 4 as positive (specificity, 97.1%; 95% CI, 92.8% to 99.2%). The median time to culture positivity was 10 days, with an interquartile range of 8 to 13 days for Auto-MODS. Auto-MODS is an effective and cost-sensitive alternative diagnostic tool for TB diagnosis in resource-limited settings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".