Stroke and its correlates among patients on maintenance hemodialysis in Cameroon
Bibliographic record
Abstract
BACKGROUND: End-stage kidney disease is an independent risk factor for stroke; however, the relationship between hemodialysis and stroke in Sub-Saharan Africa has not been established. OBJECTIVE: To evaluate the incidence, associated factors, and clinical outcome of stroke among patients undergoing maintenance hemodialysis in Cameroon. METHODS: A hospital-based retrospective study using data from the medical files of 1060 patients on maintenance hemodialysis (given twice a week) was conducted. Patients with stroke prior to starting hemodialysis were excluded. Socio-demographic data, comorbidities, dialysis parameters, and data concerning the diagnosis of stroke were retrieved and analyzed. RESULTS: The dialysis vintage (duration of time on dialysis) averaged 11.4 ± 9.2 months. The incidence of stroke was 6.1 events per 1000 patient-years, with hemorrhagic stroke being most common (66%). Eighty percent of strokes occurred before the 30th month of dialysis. Sixty percent of strokes occurred within 24 h of a dialysis session. Predictive factors for stroke were diabetes mellitus (p = 0.026), heart failure (p = 0.045), poor dialysis compliance (p = 0.001), and short vintage (p = 0.001). The overall mortality rate was 52% and was higher for hemorrhagic stroke (60%). The leading causes of death were multiple organ failure and sepsis. CONCLUSION: The incidence of stroke is high among hemodialysis patients in Cameroon and hemorrhagic stroke is the commonest type. Diabetes and heart failure triple the risk of stroke. Mortality in patients who suffered a stroke was high.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".