Evaluation of GeneXpert MTB/Rif Ultra assay performance on formalin-fixed paraffin-embedded tissues for Mycobacterium tuberculosis detection
Bibliographic record
Abstract
(MTB) detection in formalin-fixed paraffin-embedded tissue (FFPET) compared to mycobacterial culture or laboratory-developed MTB PCR test (LDT). FFPET samples with histological features suggestive of tuberculosis from 2018 to 2023 were selected. Five hundred microlitres of tissue lysis buffer was added to FFPET scrolls and incubated at 75 °C for 5 min. After adding 50 µl of proteinase K and overnight incubation at 56 °C, sample aliquots were processed as per the manufacturer's instructions. MTB culture or LDT assay results were used as a reference for sensitivity and specificity calculations. Of 51 eligible FFPET, 32 were positive for MTB either by culture or LDT PCR on FFPET. Xpert MTB/Rif Ultra detected MTB in 23/32 positive specimens [71.9%, 95% confidence interval (CI) 54.6-84.4%]. Of nine discordant specimens, seven were MTB positive by culture and two were identified by LDT MTB PCR only, as no specimen was submitted for MTB culture. Of 19 negative samples, 100% specificity (95% CI 83.2-100.0%) was attained via Xpert MTB/Rif Ultra. Implementation of Xpert MTB/Rif Ultra on FFPET within clinical laboratories is promising, given its improved turnaround time compared to MTB culture and ability to detect MTB in cases where no tissue is available for culture.
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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 | 0.007 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".