Implementation of the Ebola Virus Persistence in Ocular Tissues and Fluids (EVICT) study: Lessons learned for vision health systems strengthening in Sierra Leone
Bibliographic record
Abstract
BACKGROUND: Following the West African Ebola virus disease (EVD) outbreak of 2013-2016 and more recent EVD outbreaks in the Democratic Republic of Congo, thousands of EVD survivors are at-risk for sequelae including uveitis, which can lead to unremitting inflammation and vision loss from cataract. Because of the known risk of Ebola virus persistence in ocular fluid and the need to provide vision-restorative, safe cataract surgery, the Ebola Virus Persistence in Ocular Tissues and Fluids (EVICT) Study was implemented in Sierra Leone. During implementation of this multi-national study, challenges included regulatory approvals, mobilization, community engagement, infection prevention and control, and collaboration between multiple disciplines. In this report, we address the multifacted approach to address these challenges and the impact of implementation science research to address an urgent clinical subspecialty need in an outbreak setting. METHODOLOGY/PRINCIPAL FINDINGS: Given the patient care need to develop a protocol to evaluate ocular fluid for Ebola virus RNA persistence prior to cataract surgery, as well as protocols to provide reassurance to ophthalmologists caring for EVD survivors with cataracts, the EVICT study was designed and implemented through the work of the Ministry of Health, Sierra Leone National Eye Programme, and international partnerships. The EVICT study showed that all 50 patients who underwent ocular fluid sampling at 19 and 34 months, respectively, tested negative for Ebola virus RNA. Thirty-four patients underwent successful cataract surgery with visual acuity improvement. Here we describe the methodology for study implementation, challenges encountered, and key issues that impacted EVD vision care in the immediate aftermath of the EVD outbreak. Key aspects of the EVICT study included defining the pertinent questions and clinical need, partnership alignment with key stakeholders, community engagement with EVD survivor associations, in-country and international regulatory approvals, study site design for infection prevention and control, and thorough plans for EVD survivor follow-up care and monitoring. Challenges encountered included patient mobilization owing to transportation routes and distance of patients in rural districts. Strong in-country partnerships and multiple international organizations overcame these challenges so that lessons learned could be applied for future EVD outbreaks in West and Central Africa including EVD outbreaks that are ongoing in Guinea and Democratic Republic of Congo. CONCLUSIONS/SIGNIFICANCE: The EVICT Study showed that cataract surgery with a protocol-driven approach was safe and vision-restorative for EVD survivors, which provided guidance for EVD ophthalmic surgical care. Ophthalmologic care remains a key aspect of the public health response for EVD outbreaks but requires a meticulous, yet partnered approach with international and local in-country partners. Future efforts may build on this framework for clinical care and to improve our understanding of ophthalmic sequelae, develop treatment paradigms for EVD survivors, and strengthen vision health systems in resource-limited settings.
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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 | 0.048 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".