Challenges in preparing and implementing a clinical trial at field level in an Ebola emergency: A case study in Guinea, West Africa
Bibliographic record
Abstract
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.
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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 | 0.092 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.011 | 0.008 |
| 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".