Endovascular Treatment of Acute Ischemic Stroke in Clinical Practice: Analysis of Workflow and Outcome in a Tertiary Care Center
Bibliographic record
Abstract
Background and Purpose: Pre- and intra-hospital workflow in mechanical recanalization of large cervicocephalic arteries in patients with acute ischemic stroke still needs optimization. In this study, we analyze workflow and outcome in our routine care of stroke patients undergoing mechanical thrombectomy as a precondition for such optimization. Methods: Processes of pre- and intra-hospital management, causes of treatment delay, imaging results (Alberta Stroke Program Early Computed Tomography Score, localization of vessel occlusion), recanalization (modified thrombolysis in cerebral infarction score), and patient outcome (modified Rankin scale at discharge and at the end of inpatient rehabilitation) were analyzed for all patients who underwent mechanical thrombectomy between April 1, 2016, and September 30, 2018, at our site. Results: Finally, data of 282 patients were considered, of whom 150 (53%) had been referred from external hospitals. Recanalization success and patient outcome were similar to randomized controlled thrombectomy studies and registries. Delay in treatment occurred when medical treatment of a hypertensive crisis, epileptic fits, vomiting, or agitation was mandatory but also due to missing prenotification of the hospital emergency staff by the rescue service, multiple mode or repeated brain imaging, and transfer from another hospital. Even transfer from external hospitals located within a 10-km radius of our endovascular treatment center led to a median increase of the onset-to-groin time of ~60 min. Conclusion: The analysis revealed several starting points for an improvement in the workflow of thrombectomy in our center. Analyses of workflow and treatment results should be carried out regularly to identify the potential for optimization of operational procedures and selection criteria for patients who could benefit from endovascular treatment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".