How is Xpert MTB/RIF being implemented in 22 high tuberculosis burden countries?
Bibliographic record
Abstract
Accurate and rapid diagnosis is crucial for tuberculosis control by ensuring a timely start to treatment and reducing transmission. In 2012, almost one third of tuberculosis cases were not diagnosed and/or reported to national tuberculosis programmes (NTPs), and <25% of estimated incident multidrug-resistant (MDR) cases were diagnosed [1]. Xpert MTB/RIF (Cepheid, Sunnyvale, CA, USA), a nucleic acid amplification test, was recommended in 2010 by the World Health Organization (WHO) for detection of HIV-associated pulmonary tuberculosis and rifampicin resistance [2]. In 2013, the test was recommended for detection of paediatric tuberculosis and some forms of extrapulmonary tuberculosis (EPTB), as well as an initial test to replace smear microscopy [3]. Xpert MTB/RIF implementation is mainly donor-funded, focused on DST and is not widely used outside South Africa We are grateful to all NTP staff and respondents who completed the surveys and answered our follow-up questions. We are also grateful to Claudia Denkinger (Foundation for Innovative New Diagnostics, Geneva, Switzerland), Sandra Kik (KNCV Tuberculosis Foundation, The Hague, The Netherlands) and Pamela Chedore (independent laboratory consultant, Toronto, Canada) for useful input on the survey design.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".