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Record W2793177905 · doi:10.1186/s13104-018-3263-3

Research priorities during infectious disease emergencies in West Africa

2018· article· en· W2793177905 on OpenAlexaff
Morẹ́nikẹ́ Oluwátóyìn Foláyan, Bridget Haire, Dan Allman, Aminu Yakubu, Muhammed O. Afolabi

Bibliographic record

VenueBMC Research Notes · 2018
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersWellcome TrustWellcome
KeywordsDelphi methodOutbreakInfectious disease (medical specialty)Psychological interventionDiseaseMedicineCase fatality rateDelphiEnvironmental healthNursingComputer sciencePathology

Abstract

fetched live from OpenAlex

OBJECTIVES: This paper presents the results of the consultations conducted with various stakeholders in Africa and other experts to document community perspectives on the types of research to be prioritised in outbreak conditions. The Delphi method was used to distill consensus. RESULTS: Our consultations highlighted as key, the notion that in an infectious disease outbreak situation, the need to establish an evidence base on how to reduce morbidity and mortality in real time takes precedence over the production of generalizable knowledge. Research studies that foster understanding of how disease transmission could be prevented in the future remain important, implementation research that explores how to mitigate the impact of outbreaks in the present should be prioritized. Clinical trials aiming to establish the safety profile of therapeutic interventions should be limited during the acute phase of an epidemic with high fatality-and should preferably use adaptive designs. We concluded that community members have valuable perspectives to share about research priorities during infectious disease emergencies. Well designed consultative processes could help identify these opinions.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.263
GPT teacher head0.480
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2018
Admission routes1
Has abstractyes

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