Implementation of Xpert MTB/RIF in 22 high tuberculosis burden countries: are we making progress?
Bibliographic record
Abstract
By the end of 2016, approximately 23 million Xpert MTB/RIF® (Xpert; Cepheid, Sunnyvale, CA, USA) cartridges for tuberculosis (TB) diagnosis had been procured by the public sector in 130 countries at concessional pricing [1], but smear microscopy continues to be the most widely used test for TB [2]. To understand the true market penetration of Xpert in TB high burden countries (HBCs), we surveyed National TB Programmes (NTPs) or their partnering organisations in 22 HBCs to obtain Xpert data from 2015 and to assess dynamic trends from 2014 to 2015. These 22 countries had been previously surveyed by us in 2014 [3]. Uptake of Xpert in 22 high burden countries has progressed well since 2014, although more can be done to reach scale We are grateful to survey respondents from 22 countries for their time and support.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".