Xpert MTB/RIF as add-on test to microscopy in a low tuberculosis incidence setting
Bibliographic record
Abstract
Tuberculosis (TB) is a major public health concern worldwide. Early diagnosis, universal access to drug susceptibility testing and prompt initiation of treatment are key elements of the End TB strategy, and should therefore be implemented in all settings [1–5]. In order to reach TB elimination goals, the World Health Organization (WHO) currently recommends the use of a rapid molecular test, Xpert MTB/Rif (Xpert; Cepheid, Sunnyvale, CA, USA), as initial diagnostic tool when TB is suspected [6–8]. Although the excellent performance of this test in high TB burden areas is already supported by strong scientific evidence, few studies have been conducted so far to assess its impact on the diagnostic work-up of TB in low burden settings, sometimes with contrasting findings [7, 9, 10–12]. For example, according to Sohn et al. [10], Xpert testing might have limited impact in the ambulatory setting in Canada, owing to lower sensitivity and limited potential to expedite diagnosis beyond what is achieved with the existing, well-performing diagnostic algorithm. Xpert MTB/Rif should be used as an alternative test for microscopy for TB diagnosis in low incidence 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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.024 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".