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Record W4233779689 · doi:10.12688/gatesopenres.12842.1

Market penetration of Xpert MTB/RIF in high tuberculosis burden countries: A trend analysis from 2014 - 2016

2018· preprint· en· W4233779689 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGates Open Research · 2018
Typepreprint
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
FundersBill and Melinda Gates Foundation
KeywordsGeneXpert MTB/RIFMedicineTuberculosisSputumDisease controlEnvironmental healthPathology

Abstract

fetched live from OpenAlex

<ns4:p> <ns4:bold>Background:</ns4:bold> Xpert® MTB/RIF, a rapid tuberculosis (TB) molecular test, was endorsed by the World Health Organization in 2010. Since then, 34.4 million cartridges have been procured under concessional pricing. Although the roll out of this diagnostic is promising, previous studies showed low market penetration. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> To assess 3-year trends of market penetration of Xpert MTB/RIF in the public sector, smear and Xpert MTB/RIF volumes for the year 2016 were assessed and policies from 2014-2016 within 22 high-burden countries (HBCs) were studied. A structured questionnaire was sent to representatives of 22 HBCs. The questionnaires assessed the total smear and Xpert MTB/RIF volumes, number of modules and days of operation of GeneXpert machines in National TB Programs (NTPs). Data regarding the use of NTP GeneXpert machines for other diseases and GeneXpert procurement by other disease control programs were collected. Market penetration was estimated by the ratio of total sputum smear volume for initial diagnosis divided by the number of Xpert MTB/RIF tests procured in the public sector. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> The survey response rate was 21/22 (95%). Smear/Xpert ratios decreased in 17/21 countries and increased in four countries, since 2014. The median ratio decreased from 32.6 (Q1:14.3, Q3: 58.9) in 2014 to 6.0 (Q1: 1.6, Q3: 17.0) in 2016. Nineteen countries (19/19; 100%) were not using GeneXpert machines to their full capacity, however seven countries (7/19; 37%) were running tests for other diseases on their NTP-procured GeneXpert systems in 2017, such as HIV, hepatitis-C virus (HCV), <ns4:italic>Chlamydia trachomatis</ns4:italic> , and <ns4:italic>Neisseria gonorrhoeae</ns4:italic> . Five (5/15; 33%) countries reported GeneXpert procurement by HIV or HCV programs in 2016 and/or 2017. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> Our results show a positive trend for Xpert MTB/RIF market penetration in 21 HBC public sectors. However, GeneXpert machines were under-utilized for TB, and inadequately exploited as a multi disease technology. </ns4:p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0230.001

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.

Opus teacher head0.074
GPT teacher head0.425
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it