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Record W4408218838 · doi:10.1186/s41256-025-00408-y

Establishment of a regional Mpox surveillance network in Central Africa: shared experiences in an endemic region

2025· article· en· W4408218838 on OpenAlexaff
Emmanuel Hasivirwe Vakaniaki, Sydney Merritt, Sylvie Linsuke, Emile Malembi, Francisca Muyembe, Lygie Lunyanga, Andrea Mayuma, Papy Kwete, Thierry Kalonji-Mukendi, Joule Madinga, Matthew LeBreton, Emmanuel Nakouné, Ernest Kalthan, Sevidzem Shang, Julius Nwobegahay, Odianosen Ehiakhamen, Elsa Dibongue, Jean-Médard Kankou, Bernard Erima, Denis K. Byarugaba, Paige Rudin Kinzie, Franck Mebwa, Francis Baelongandi, Aimé Kayolo, Pepin S. Nabugobe, Dieudonné Mwamba, Jean Malekani, Béatrice Nguete, Didine Kaba, Lisa E. Hensley, Jason Kindrachuk, Laurens Liesenborghs, Robert Shongo, Jean-Jacques Muyembe-Tamfum, Nicole A. Hoff, Anne W. Rimoin, Placide Mbala‐Kingebeni

Bibliographic record

VenueGlobal Health Research and Policy · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsUniversity of Manitoba
FundersDefense Threat Reduction AgencyCenters for Disease Control and Prevention
KeywordsPublic healthEnvironmental healthGeographyPolitical scienceEconomic growthMedicineNursing

Abstract

fetched live from OpenAlex

To address the underreporting of mpox cases in endemic regions, a regional surveillance network, known as the Mpox Threat Reduction Network (MPX-TRN), was established between five neighboring countries in Central and West Africa in 2022. One direct outcome of the MPX-TRN has been the strengthening of regional mpox surveillance. This consortium has facilited open communication channels, detection of cross-border mpox cases, and improvements of the detection and diagnosis of mpox in Central Africa and worldwide. Importantly, the MPX-TRN provides a scalable model for addressing underreporting of diseases, such as mpox.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0030.003
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.096
GPT teacher head0.431
Teacher spread0.335 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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