Risk factors of intracerebral hemorrhage- a cross sectional study
Bibliographic record
Abstract
BACKGROUND:
 Stroke is the most common cause of disability and a leading cause of mortality worldwide. Though the incidence is falling in West but probably rising in Asia. The burden of stroke risk factors in Pakistan is enormous. Data on stroke incidence and prevalence from Pakistan is scarce; however, there are several reported case series in literature highlighting significant differences in terms of stroke epidemiology, risk factors and stroke subtypes/patterns. METHODS: This descriptive cross sectional study was conducted from August 2019 to February 2020, on 109 patients from medical units of DHQ Teaching Hospital Abbottabad. Diagnosis of cerebrovascular accidents was made on focal neurological deficit lasting more than 24 hours. CT scan brain was done in all patients to detect intra cerebral bleed. Detailed history and medical records were carefully scrutinized in the patients of intracerebral bleed to detect factors leading to it like uncontrolled hypertension. Fasting blood glucose, fasting serum cholesterol and fasting triglycerides were done to detect uncontrolled diabetes and hyperlipidemia. Data was collected on a structured proforma and analysed using SPSS 20. RESULTS: Majority of the patients were 39.81%(n=41) >70 years of age, 71 (68.93%) were male and 32 (31.07%) female, frequency of intracerebral bleed among patients presenting with acute cerebrovascular accidents was recorded in 8.74%(n=9), among them 66.67%(n=6) had history of uncontrolled hypertension, 44.44%(n=4) had diabetes and 33.33%(n=3) had hyperlipidemia.
 CONCLUSIONS: Frequency of intracerebral bleed is higher among patients presenting with cerebrovascular accidents, hypertension is recorded the most common risk factor for this complication.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".