Development, roll-out and impact of Xpert MTB/RIF for tuberculosis: what lessons have we learnt and how can we do better?
Bibliographic record
Abstract
The global roll-out of Xpert MTB/RIF (Cepheid Inc., Sunnyvale, CA, USA) has changed the diagnostic landscape of tuberculosis (TB). More than 16 million tests have been performed in 122 countries since 2011, and detection of multidrug-resistant TB has increased three- to eight-fold compared to conventional testing. The roll-out has galvanised stakeholders, from donors to civil society, and paved the way for universal drug susceptibility testing. It has attracted new product developers to TB, resulting in a robust molecular diagnostics pipeline. However, the roll-out has also highlighted gaps that have constrained scale-up and limited impact on patient outcomes. The roll-out has been hampered by high costs for under-funded programmes, unavailability of a complete solution package (notably comprehensive training, quality assurance, implementation plans, inadequate service and maintenance support) and lack of impact assessment. Insufficient focus has been afforded to effective linkage to care of diagnosed patients, and clinical impact has been blunted by weak health systems. In many countries the private sector plays a dominant role in TB control, yet this sector has limited access to subsidised pricing. In light of these lessons, we advocate for a comprehensive diagnostics implementation approach, including increased engagement of in-country stakeholders for product launch and roll-out, broader systems strengthening in preparation for new technologies, as well as quality impact data from programmatic 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 | 0.044 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".