Challenging the gold standard: critical limitations in clinical detection of drug-resistant tuberculosis
Bibliographic record
Abstract
Abstract Heteroresistant infections - defined as infections in which minority drug-resistant (DR) populations are present - are a challenge in infectious disease control. In Mycobacterium tuberculosis , heteroresistance poses challenges in diagnosis and has been linked with poor treatment outcomes. We compared the analytic sensitivity of molecular methods, such as GeneXpert and whole genome sequencing (WGS) in detecting heteroresistance when compared to the ‘gold standard’ phenotypic assay: the agar proportion method (APM). Using defined mono-resisitant BCG strains we determined the limit of detection (LOD) of rifampin-R (RIF-R) detection was 1% using APM, 60% using Xpert MTB/RIF and 10% using Xpert MTB/RIF Ultra. To evaluate clinical WGS pipelines, a blinded panel of BCG mixtures was sent to 3 clinical labs. These were composed of either a) RIF-R plus isoniazid-R (INH-R) BCG or b) fluoroquinolone-R (FQ-R) plus clofazimine-R/bedaquiline-R (CLZ/BDQ-R) BCG. No labs called resistance at 1%; all labs called RIF-R at 10% or greater and two out of three labs reported FQ-R at 10%. Two labs were able to detect the majority population (either INH-R or CLZ/BDQ-R) at 50%. Importantly, where labs did not report resistance in the majority population, the mutations were present in the raw data but excluded from the final analysis. In conclusion, the gold standard APM more reliably detects minority resistant populations than molecular tests. Further research is required to determine whether the higher LOD of molecular tests is associated with deleterious patient outcomes and the potential effects on transmission of resistance at the population level.
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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 | 0.328 | 0.313 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".