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 <http://ow.ly/CK4NS> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".