Revisiting Africa’s Stroke Obstacles and Services (SOS)
Bibliographic record
Abstract
BACKGROUND: As one of the most common non-communicable diseases in Africa, Stroke ought to be dealt with properly with intensifying efforts to control its burden and to face obstacles in its management. METHODS AND RESULTS: In this follow-up study we reanalyzed stroke services and related obstacles in 17 African countries that were previously studied in 2021/22 in aspects related to manpower, acute stroke services, rehabilitation programs, number of stroke units/centers, telestroke services, awareness campaigns, and national and international stroke registries through a survey that was sent to stroke specialists and national stroke societies. Overall, there is an improvement in many fields yet many obstacles in the implementation of telestroke services, acute management, secondary prevention, post-discharge services, and follow-ups whether governmental, medical, or societal are prevalent. CONCLUSION: Stroke services in Africa are improving in 2024 compared to 2021/22 in many fields, stationary in some fields, and regressing in a few. Managing obstacles that are raised by stroke specialists collectively and on individual countries basis will pave the way for better services for the wellness of stroke victims in Africa.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".