Another form of Lassa fever? Early neurological symptoms and high mortality reveal differences in two outbreaks in Ebonyi State, Nigeria 2017–2019
Bibliographic record
Abstract
Background: Lassa fever (LF) is an acute viral haemorrhagic illness with various clinical manifestations. Neurological symptoms are not commonly present at the early stage of the disease; however, early manifestation of central nervous system features depicts poor prognostication. In Ebonyi state, an unusual pattern was observed between two outbreaks with patients presenting early neurological symptoms and a high mortality rate in the second outbreak. The study described the epidemiological evolution, socio-demographic profiles, clinical characteristics and patients’ outcomes. Methods and materials: A retrospective analytic analysis of routinely collected clinical data was conducted of all confirmed and probable LF patients admitted to the Virology Centre of the AEFUTHA in Ebonyi State, December 2017 to January 2019. Results: In a total of 83 cases, 70 were RT-PCR confirmed and 13 probable cases. In outbreak 1, 69 were seen with 53.6% being urban residents, 19% farmers, 15% students, and 10% health workers. Fourteen cases were seen in outbreak 2 with 92.9% rural residents, 58.3% being farmers and 49.9% students. There were differences in clinical and laboratory signs and symptoms between the two outbreaks with neurological symptoms present 43% of the time in outbreak 1 and 93% in outbreak 2 (p = 0.001), with a shorter time of onset for these symptoms in outbreak 2. The mortality rate was 85.7% in outbreak 2 versus 29.9% in outbreak 1 (p < 0.001). Patients with neurological symptoms, who were more common in outbreak 2 had a RR of dying of 8.5 compared to those without. Conclusion: This study revealed a different form of LF that is of great concern due to its high mortality rate. Further studies are needed to better define its characteristics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".