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Record W2663914633 · doi:10.1371/journal.pntd.0005545

Challenges in preparing and implementing a clinical trial at field level in an Ebola emergency: A case study in Guinea, West Africa

2017· article· en· W2663914633 on OpenAlexaff
Sara Carazo, Elin Folkesson, Xavier Anglaret, Abdoul-Habib Beavogui, Emmanuel Berbain, Alseny-Modet Camara, Evelyn Depoortere, Annabelle Lefevre, Piet Maes, Kristian Nødtvedt Malme, Sien Ombelet, Geertrui Poelaert, Daouda Sissoko, Alexis Tounkara, Pierre Trbovic, Pascal Piguet, Annick Antierens

Bibliographic record

VenuePLoS neglected tropical diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEbola virusClinical trialInformed consentConfidentialityMedicineFavipiravirEbola Hemorrhagic FeverResearch ethicsFamily medicineAlternative medicineMedical educationDiseasePolitical scienceOutbreakInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)VirologyPathologyLaw

Abstract

fetched live from OpenAlex

During the large Ebola outbreak that affected West Africa in 2014 and 2015, studies were launched to evaluate potential treatments for the disease. A clinical trial to evaluate the effectiveness of the antiviral drug favipiravir was conducted in Guinea. This paper describes the main challenges of the implementation of the trial in the Ebola treatment center of Guéckédou. Following the principles of the Good Clinical Research Practices, we explored the aspects of the community's communication and engagement, ethical conduct, trial protocol compliance, informed consent of participants, ongoing benefit/risk assessment, record keeping, confidentiality of patients and study data, and roles and responsibilities of the actors involved. We concluded that several challenges have to be addressed to successfully implement a clinical trial during an international medical emergency but that the potential for collaboration between research teams and humanitarian organizations needs to be highlighted.

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 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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.114
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.370
GPT teacher head0.480
Teacher spread0.110 · 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 teacher head, 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

Citations27
Published2017
Admission routes1
Has abstractyes

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