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Record W2136540689 · doi:10.1086/520548

The Médecins Sans Frontières Intervention in the Marburg Hemorrhagic Fever Epidemic, Uige, Angola, 2005. I. Lessons Learned in the Hospital

2007· article· en· W2136540689 on OpenAlexfundno aff
Benjamin Jeffs, Paul Roddy, David Weatherill, Olimpia de la Rosa, Claire Dorion, Marta Iscla, Isabel Grovas, Pedro Pablo Palma, Luís Villa, Óscar Bernal, Josefa Rodriguez‐Martinez, Barbara Barcelo, Diana Pou, Matthias Borchert

Bibliographic record

VenueThe Journal of Infectious Diseases · 2007
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
FundersCenters for Disease Control and PreventionPublic Health Agency of Canada
KeywordsVirologyMedicineCrimean–Congo hemorrhagic fever

Abstract

fetched live from OpenAlex

From 27 March 2005 onwards, the independent humanitarian medical aid agency Medecins Sans Frontieres, together with the World Health Organization, the Angolan Ministry of Health, and others, responded to the Marburg hemorrhagic fever (MHF) outbreak in Uige, Angola, to contain the epidemic and care for those infected. This response included community epidemiological surveillance, clinical assessment and isolation of patients with MHF, safe burials and disinfection, home-based risk reduction, peripheral health facility support, psychosocial support, and information and education campaigns. Lessons were learned during the implementation of each outbreak control component, and the subsequent modifications of protocols and strategies are discussed. Similar to what was seen in previous filovirus hemorrhagic fever outbreaks, the containment of the MHF epidemic depended on the collaboration of the affected community. Actively involving all stakeholders from the start of the outbreak response is crucial.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.024
GPT teacher head0.356
Teacher spread0.331 · 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

Citations84
Published2007
Admission routes1
Has abstractyes

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