Research priorities during infectious disease emergencies in West Africa
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.153 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".