Neurological manifestations in patients with symptomatic COVID-19 admitted to the Bafoussam Regional Hospital, Cameroon
Bibliographic record
Abstract
INTRODUCTION: although the main manifestations of COVID-19 are respiratory, several neurological symptoms and complications have also been reported. The pandemic seems to have some epidemiological specificities in sub-Saharan Africa, and this may be reflected in the type and frequency of neurological symptoms. This study aimed to report neurological manifestations associated with symptomatic COVID-19 in a sub-Saharan African setting. METHODS: we conducted a retrospective review of symptomatic PCR-confirmed COVID-19 cases admitted to the Bafoussam Regional Hospital between March and September 2020. Patients' files were reviewed at discharge by a consultant neurologist. Socio-demographic characteristics, co-morbidities, symptoms on admission, neurological symptoms during hospitalization, management, and in-hospital outcome were recorded. Comparisons between patients with and without neurological symptoms were performed using Fisher's exact and Mann-Whitney U test. RESULTS: we enrolled 177 symptomatic patients (68% men). Mean age was 54.6 ± 17.8 years (range 2-99 years). Co-morbidities were present in 57.6% of patients, including hypertension (27.1%) and diabetes mellitus (25.4%). Neurological symptoms were found in 113 (63.8%) patients. The most frequent were headache (39.0%), myalgia (35.6%), anosmia (11.9%), impaired consciousness (10.7%) and delirium (5.6%). Regarding the presenting symptoms, fever was more frequent in patients with neurological symptoms than in those without (81.4% versus 50.0%, p< 0.001), while digestive symptoms were less frequent in patients with neurological symptoms (0.9% versus 9.4%, p= 0.004). CONCLUSION: neurological manifestations are frequent and heterogeneous in patients with symptomatic COVID-19. Further studies are needed to clarify the pathophysiology of neurological symptoms in COVID-19 and their impact on patients' long-term outcome.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".