The Burden of Inpatient Neurologic Disease in a Tropical African Hospital
Bibliographic record
Abstract
BACKGROUND: Neurologic disorders represent a major burden of disease globally and the spectrum ranges from noncommunicable disorders like stroke and neurodegenerative disorders to central nervous system infections. OBJECTIVE: The purpose of the study is to assess the burden of neurological diseases in a tropical environment. METHODS: A one year retrospective survey of neurological diseases seen at the University of Calabar Teaching Hospital, Nigeria, was evaluated using patients' medical record. RESULTS: Neurological diseases constituted 24.2% of all medical conditions seen over a one year period. Stroke was found to be the commonest cause of admissions accounting for 42.1% of the cases followed by peripheral neuropathy (13.8%) and meningoencephalitis (7.2%). The immediate case fatality rate was 33.6%. Fifty two percent were discharged home with various levels of recovery while 12.5% left against medical advice. About 2% were referred to other tertiary health institutions. CONCLUSION: The pattern of neurologic diseases in the local medical wards was not remarkably different from those observed in Nigeria and elsewhere. Stroke remains the most frequent cause of neurologic admissions and mortality in this region is same as observed elsewhere.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".