Incidence and Predictors of Post Stroke Seizure among Adult Stroke Patients in Western Amhara Region, Ethiopia, 2021: A Retrospective Follow up Study
Bibliographic record
Abstract
Abstract Background A seizure is an episode of neurological dysfunction caused by abnormal neuronal activity. Post stroke seizure affects patients’ lives and increase mortality in patients with stroke. It also negatively affects the prognosis of stroke. However, literatures on the occurrence of post-stroke seizure are scanty in Ethiopia. Therefore, this study aimed to assess the incidence and predictors of post-stroke seizure in west Amhara Region, Ethiopia, 2021. Methods An institution-based retrospective follow-up study was conducted at western Amhara region from September 20/2021, to October 10 / 2021. Patients who included in this study were come from western Amhara region and that admitted at Felege Hiwot compressive specialized hospital stroke care center. Charts of 568 stroke patients were reviewed through a simple random sampling technique. Descriptive statistics and log-binomial regressions model were applied. Result The cumulative incidence and incidence density rate of seizure were 22.18% and 37/1000 persons per day observation respectively. Older age group (ARR = 2.49, 95% CI 1.33–4.69), haemorrhagic 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 as the significant risk factors of post stroke seizure. Conclusion This study found that older age, hemorrhagic type of stroke, surgical management and use of Tramadol anti-pain medication were found as risk factors that increase the risk of post-stroke seizure. Thus, health care professionals shall give special attention and clinical care accordingly for patients with risk factors of post-stroke seizure.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".