Early post-acute stroke seizures: Clinical profile and outcome in a Nigerian stroke unit
Bibliographic record
Abstract
AIM: To describe the basic clinico-demographic profile and outcome of Early Post-Acute Stroke Seizures (EPASS). MATERIALS AND METHODS: Two-hundred and fifty one patients admitted within 24 h of onset of stroke symptoms into the stroke unit of a tertiary care hospital were followed up for convulsive seizure(s) within 7 days of admission and for disease outcome in 42 days. Stroke subtype was defined by cranial computed tomography and ictal phenomenon was as described by the stroke unit doctors. Stroke severity was by the Canadian Neurological Scale (CNS) and Glasgow Coma Scale (GCS). Seizures were characterized as partial, generalized, or status. Stroke outcome was defined as discharge from inpatient care to follow-up or still in care and all cause in-hospital death. Data was compared between the group with and without seizures. The effect of age, sex, blood sugar, GCS, CNS, and seizure type on stroke outcome and time to in-hospital death in EPASS was tested on logistic regression and Cox proportional hazard regression. RESULT: EPASS occurred in 9.96% of subjects and intracerebral infarct was more associated with EPASS, a finding different from what is dominant in western literature. CONCLUSION: Profile of EPASS may appear different in terms of stroke subtype in Sub-Saharan African populations. Larger prospective studies may clarify the position better.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".