MENA-SINO Consensus Statement on Implementing Care Pathways for Acute Neurovascular Emergencies During the COVID-19 Pandemic
Bibliographic record
Abstract
In the unprecedented current era of the COVID-19 pandemic, challenges have arisen in the management and interventional care of patients with acute stroke and large vessel occlusion, aneurysmal subarachnoid hemorrhage, and ruptured vascular malformations. There are several challenges facing endovascular therapy for stroke, including shortages of medical staff who may be deployed for COVID-19 coverage or who may have contracted the infection and are thus quarantined, patients avoiding early medical care, a lack of personal protective equipment, delays in door-to-puncture time, anesthesia challenges, and a lack of high-intensity intensive care unit and stroke ward beds. As a leading regional neurovascular organization, the Middle East North Africa Stroke and Interventional Neurotherapies Organization (MENA-SINO) has established a task force composed of medical staff and physicians from different disciplines to establish guiding recommendations for the implementation of acute care pathways for various neurovascular emergencies during the current COVID-19 pandemic. This consensus recommendation was achieved through a series of meetings to finalize the recommendation.
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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 | 0.098 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.033 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".