Retrospective Cohort Analysis for Identification of Discordant Rifampicin-resistant Xpert MTB/RIF Assay Results in South Kivu, Eastern Democratic Republic of the Congo, a High Burden Tuberculosis Setting
Bibliographic record
Abstract
BACKGROUND: The Xpert assay has revolutionized the rapid detection of resistance to rifampicin. However, Xpert has its pitfalls. We explored potential determinants of false-positive rifampicin resistance when using Xpert, aiming to refine the precision of tuberculosis diagnostics and subsequently contribute to better patient outcomes. METHODS: This is a retrospective cross-sectional analysis of archived Xpert files from the South Kivu province, used to diagnose Mycobacterium tuberculosis (MTB) between 2013 and 2018. Xpert cycle threshold was extracted for each molecular beacon probe, and ΔCt was calculated. We used the MTBDRplus line-probe assay, which covers the same 81-bp RRDR, as a reference test. RESULTS: Of 1900 samples positive for MTB, 220 (11.2%) were rifampicin resistant. Of the 141 patients' sputum samples that had results for both Xpert and MTBDRplus, 45 (31.9%) showed discordant results with Xpert, indicating rifampicin resistance while MTBDRplus indicated rifampicin susceptibility, suggesting false-positive rifampicin resistance detection by Xpert, predominantly in samples with very low (Ct > 28, odds ratio [OR] = 2.23, 95% CI: 1.30-3.82) or low (Ct 22-28, OR = 1.81, 95% CI: 1.21-2.71) bacterial loads. Probe E was the most frequently missed probe, followed by multiple probe dropouts or absence of probe binding (OR = 1.5, 95% CI: .731-3.076). CONCLUSIONS: Our findings indicate that low and very low MTB bacterial loads in sputum are strongly associated with discordant rifampicin resistance results when using Xpert. Further research into underlying mechanisms is needed to establish causality definitively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".