Incidence and predictors of post stroke seizure among adult stroke patients admitted at Felege Hiwot compressive specialized hospital, Bahir Dar, North West Ethiopia, 2021: a retrospective follow up study
Bibliographic record
Abstract
BACKGROUND: A post stroke seizure is a period of neurological dysfunction caused by abnormal neuronal activity. Seizures after a stroke have an impact on patients' lives and increase mortality in stroke patients. It also has a negative impact on the prognosis of stroke. However, there is a scarcity of literature in Ethiopia on the occurrence of post-stroke seizures. Therefore, this study aimed to assess the incidence and predictors of post-stroke seizure at Felege Hiwot compressive specialized hospital, North West Ethiopia. METHODS: An institution-based retrospective follow-up study was carried out at Felege Hiwot Compressive Specialized Hospital, North West Ethiopia from July 1, 2017 to June 30, 2021. The records of 568 stroke patients were reviewed using a random sample method. To find predictors of post-stroke seizures, we applied the log-binomial regression model. RESULT: The incidence of post-stroke seizures was 22.18%( 95% CI 18.83%-25.83%). Older age group (ARR = 2.49, 95% CI 1.33-4.69), hemorrhagic stroke (ARR = 1.99, 95% CI 1.25-3.17), surgical intervention (ARR = 1.85, 95% CI 1.22-2.81), and tramadol medication (ARR = 1.85, 95% CI 1.22-2.81) were found to be predictors of post stroke seizure. CONCLUSION: This study revealed that the incidence of post stroke seizure was high and older age, haemorrhagic type of stroke, surgical management, and use of tramadol anti-pain medication are risk factors for post-stroke seizures. Therefore, health care professionals must pay special attention and provide clinical care to patients who have risk factors for post-stroke seizure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".