Neurological Manifestations in Oropouche Virus Infection: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Oropouche virus (OROV), an emerging arbovirus, poses a significant public health concern in tropical and subtropical regions of Latin America, as well as in other parts of the world, with imported cases reported in North America and Europe. While OROV is primarily associated with acute febrile illness, especially emerging evidence suggests it may cause neurological complications, though these remain understudied. This systematic review and meta-analysis aim to estimate the prevalence of neurological manifestations in OROV infections. Following the PRISMA 2020 guidelines, a systematic literature search was conducted across PubMed, Web of Science, and Embase up to January 25, 2025, and registered in PROSPERO (Registration ID: CRD42025634617). Nested Knowledge software was employed for the screening and data extraction processes. Data extraction and quality assessment were performed using a modified version of the Newcastle-Ottawa Scale. A meta-analysis was conducted using R software to estimate the pooled prevalence rates of neurological manifestations, with heterogeneity assessed using the I² statistic. Sensitivity analyses and publication bias assessments were also performed. Ten studies from Brazil, Peru, and Colombia were included, encompassing a total of 2872 patients. The pooled prevalence of neurological symptoms was high, with headache (89.16%), myalgia (70.71%), and eye pain (52.87%) being the most common. Other symptoms included arthralgia (56.5%), back pain (46.1%), and nausea (43.3%). Significant heterogeneity was observed across studies, likely due to variations in geography and diagnostic methods. Sensitivity analyses confirmed the robustness of the findings. Neurological manifestations are prevalent in OROV infections, with headache, myalgia, and eye pain being the most frequent. The clinical overlap with other arboviruses complicates diagnosis, underscoring the need for improved diagnostic tools and surveillance of neurological syndromes associated with arboviruses in endemic regions and newly emerging areas with recent circulation of the virus. To improve generalizability, future research should broaden geographic analyses and concentrate on longitudinal and standardized studies to better understand the temporal dynamics of symptoms.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".