Stroke units in Nigeria: a report from a Nationwide organizational cross-sectional survey
Bibliographic record
Abstract
Introduction: stroke is one of the leading causes of death and disability in Nigeria. Stroke unit care is crucial for reducing mortality and morbidity in stroke. This study describes the stroke units' structure, organization, and care process in Nigerian tertiary hospitals. Methods: this study is a cross-sectional descriptive organizational survey-based study using an online structured questionnaire to collect information on the stroke units. Results: five (8.6%) out of 58 hospitals had a stroke unit. The number of beds ranged between 10 and 27 with the coverage of hospital stroke patients ranging from 24% to 100%. All the centers had a multidisciplinary team for their unit. The basic required investigations like computerized tomography and electrocardiography were available in the centers. Thrombolytic therapy coverage was suboptimal in all the centers due to prolonged onset-to-arrival times and inaccessibility of thrombolytic medications. Conclusion: there has been some progress in stroke unit availability since the country´s first stroke unit was established over a decade ago. However, there is still the need to create more stroke units in Nigeria and improve reperfusion therapy coverage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".