Modeling the Optimal Transportation for Acute Stroke Treatment
Bibliographic record
Abstract
PURPOSE: Prolonged transfer times between the primary stroke center (PSC) and the comprehensive stroke center (CSC) are one of the major causes of treatment delay for endovascular stroke treatment. We aimed to analyze the effect of the diurnal variations in traffic rates at weekdays and weekends on the catchment area size of three transportation paradigms, i.e. mothership, drip-and-ship (DS) and drip-and-drive (DD). METHODS: A conditional probability model that predicts the probability of good outcome for patients with suspected large vessel occlusion was used to analyze the prehospital stroke triage in northwest Germany and produce catchment area maps. Transportation times were calculated during each hour of a weekday and a Sunday using Google Maps. For comparison, real DD transportation times from our CSC in Hamburg-Eppendorf (blinded for review) to a PSC in Lüneburg were prospectively recorded. RESULT: , 48%). The DS catchment area was higher than the DD catchment area during the afternoon rush hours both during the week as well as on Sundays. CONCLUSION: Our study showed a considerable impact of the diurnal variations in traffic rate and direction of travel on optimal stroke transportation. Stroke systems of care should take real time traffic information into account.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".