Direct detection of Mycobacterium tuberculosis with nitrate reductase assay and microscopic observation drug susceptibility
Bibliographic record
Abstract
The global increase in tuberculosis drug resistant which is a threat to its control, require low cost method of diagnosis and detection. Available conventional and molecular methods consume time, and are expensive for countries with high disease burden. Nitrate Reductase Assay (NRA) and Microscopic Observation Drug Susceptibility (MODS) performance to directly detect tuberculosis resistance to four drugs was evaluated. The NRA (liquid and solid) and MODS performance of smear-positive sputum samples were evaluated; Sensitivities and specificities were compared with Proportion Method (PM). Sensitivity and specificity of liquid NRA (LNRA) were 90% and 98% (rifampicin), 81.8% and 100% (isoniazid), 88.9% and 98.1% (streptomycin), and 57.1% and 94.4% (ethambuthol). Also, the sensitivity and specificity for solid NRA (SNRA) were 69.2% and 98.3% (rifampicin); 100% and 100% (isoniazid); 88.9% and 95.2% (streptomycin); 70% and 80.6% (ethambuthol). Moreover, For MODS, rifampicin and isoniazid sensitivity and specificity was 100%, it was 100% and 98.1% for streptomycin, and 71.4% and 98.2% for ethambuthol. At day 14, the results available for LNRA, SNRA and MODS were 93%, 68.5% and 100% respectively. The agreement between LNRA and PM was 97% (RIF, INH and SM) and 90% (EMB). For SNRA, it was 93% (RIF), 100% (INH), 94% (SM) and 89% (EMB). While for MODS, it was 100% (RIF and INH), 98% (SM) and 95% (EMB). Direct NRA and MODS are sensitive, reliable and fast for antituberculosis drug susceptibility; they have potential to effectively and reliably detect drug resistant tuberculosis in the low resource countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".