Utstein recommendation for emergency stroke care
Bibliographic record
Abstract
BACKGROUND: Recent advances in treatment for stroke give new possibilities for optimizing outcomes. To deliver these prehospital care needs to become more efficient. AIM: To develop a framework to support improved delivery of prehospital care. The recommendations are aimed at clinicians involved in prehospital and emergency health systems who will often not be stroke specialists but need clear guidance as to how to develop and deliver safe and effective care for acute stroke patients. METHODS: Building on the successful implementation program from the Global Resuscitation Alliance and the Resuscitation Academy, the Utstein methodology was used to define a generic chain of survival for Emergency Stroke Care by assembling international expertise in Stroke and Emergency Medical Services (EMS). Ten programs were identified for Acute Stroke Care to improve survival and outcomes, with recommendations for implementation of best practice. CONCLUSIONS: Efficient prehospital systems for acute stroke will be improved through public awareness, optimized prehospital triage and timely diagnostics, and quick and equitable access to acute treatments. Documentation, use of metrics and transparency will help to build a culture of excellence and accountability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.068 | 0.044 |
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".