Cincinnati Prehospital Stroke Scale for EMS Redirection of Large Vessel Occlusion Stroke
Bibliographic record
Abstract
INTRODUCTION: Prehospital identification of large vessel occlusion (LVO) stroke may expedite treatment by direct transport to comprehensive stroke centers (CSCs) with endovascular capabilities. The Cincinnati Prehospital Stroke Scale (CPSS) is commonly used for prehospital stroke detection. We aimed to assess whether (1) a high CPSS score can identify LVO and (2) an Emergency Medical Service (EMS) redirection protocol based on high CPSS accelerated endovascular treatment (EVT). METHODS: A retrospective comparison of patients transported by EMSs for suspected stroke to a high-volume CSC over a 16-month period, before and after implementation of an EMS redirection protocol based on high CPSS score (3/3). Charts were reviewed to determine the presence of LVO. Time to EVT and 3-month outcomes were compared before and after implementation. RESULTS: A prehospital CPSS 3/3 score was found in 223 (59%) patients, demonstrating positive and negative predictive values for LVO of 29% and 94%, respectively. CPSS-based EMS redirection increased the proportion of EVT performed after direct transport to CSC [before: 21 (36%), after: 45 (63%), p < 0.01] and decreased median first door-to-groin puncture time by 28 minutes [109 (interquartile range (IQR) 64-116) versus 81 (IQR 56-130), p = 0.03]. At 3 months, the proportion of patients achieving functional independence (modified Rankin score 0-2) went from 20/57 (35%) to 29/68 (43%) (p = 0.39) following implementation. CONCLUSIONS: CPSS-based EMS redirection accelerated identification of LVO strokes in the out-of-hospital setting and decreased time to EVT. Nevertheless, this protocol was also associated with high rates of non-LVO stroke. Impact on clinical outcomes should be evaluated in a larger cohort.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".