Stroke services in the Middle East and adjacent region: A survey of 34 hospital-based stroke services
Bibliographic record
Abstract
Acute stroke care is complex and requires multidisciplinary networking. There are insufficient data on stroke care in the Middle East and adjacent regions in Asia and Africa.Evaluate the state of readiness of stroke programs in the Middle East North Africa and surrounding regions (MENA+) to treat acute stroke.Online questionnaire survey on the evaluation of stroke care across hospitals of MENA+ region between April 2021 and January 2022.The survey was completed by 34/50 (68%) hospitals. The median population serviced by participating hospitals was 2 million. The median admission of patients with stroke/year was 600 (250-1,100). The median length of stay at the stroke units was 5 days. 34/34 (100%) of these hospitals have 24/7 CT head available. 17/34 (50%) have emergency guidelines for prehospital acute stroke care. Mechanical thrombectomy with/without IVT was available in 24/34 (70.6%). 51% was the median (IQR; 15-75%) of patients treated with IVT within 60 min from arrival. Thirty-five minutes were the median time to reverse warfarin-associated ICH.This is the first large study on the availability of resources for the management of acute stroke in the MENA+ region. We noted the disparity in stroke care between high-income and low-income countries. Concerted efforts are required to improve stroke care in low-income countries. Accreditation of stroke programs in the region will be helpful.
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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 | 0.001 |
| 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".