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Record W4406973771 · doi:10.1016/s0140-6736(24)02669-2

Suspected and confirmed mpox cases in DR Congo: a retrospective analysis of national epidemiological and laboratory surveillance data, 2010–23

2025· article· en· W4406973771 on OpenAlexafffund
Eugene Bangwen, Ruth Diavita, Elise De Vos, Emmanuel Hasivirwe Vakaniaki, Sabin S. Nundu, Annie Mutombo, Felix Mulangu, Aaron Aruna Abedi, Emile Malembi, Thierry Kalonji-Mukendi, Cris Kacita, Eddy Kinganda-Lusamaki, Tony Wawina-Bokalanga, Cécile Kremer, Isabel Brosius, Christophe Van Dijck, Emmanuel Bottieau, Koen Vercauteren, Adrienne Amuri-Aziza, Jean-Claude Makangara-Cigolo, Elisabeth Muyamuna, Elisabeth Pukuta, Béatrice Nguete, Didine Kaba, Joelle Kabamba, Christine M. Hughes, Olivier Tshiani-Mbaya, Anne W. Rimoin, Nicole A. Hoff, Jason Kindrachuk, Niel Hens, Martine Peeters, Nicola Low, Andrea M. McCollum, Robert Shongo, Daniel Mukadi‐Bamuleka, Jean-Jacques Muyembe-Tamfum, Steve Ahuka‐Mundeke, Laurens Liesenborghs, Placide Mbala‐Kingebeni

Bibliographic record

VenueThe Lancet · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsUniversity of Manitoba
FundersEuropean Civil Protection and Humanitarian Aid OperationsEuropean and Developing Countries Clinical Trials PartnershipDepartement Economie, Wetenschap en InnovatieCanadian Institutes of Health ResearchCenters for Disease Control and PreventionDirectorate-General for International PartnershipsVlaamse regeringFonds Wetenschappelijk OnderzoekInternational Development Research CentreSanofi
KeywordsEpidemiologyMedicineRetrospective cohort studyEpidemiological surveillanceEnvironmental healthPathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.351
Teacher spread0.295 · 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 designObservational
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

Citations37
Published2025
Admission routes2
Has abstractno

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