Results of the implementation of reperfusion technologies in ischemic stroke
Bibliographic record
Abstract
INTRODUCTION: Ischemic stroke (IS) remains a serious medical and social issue due to its high prevalence, mortality, and resulting disability. Reperfusion therapy is an effective method of treating patients with IS. OBJECTIVE: The aim of this study was to examine the results of implementation of reperfusion technologies for the treatment of IS in the Russian Federation. MATERIAL AND METHODS: The analysis was based on nationwide data collected from 2015 through the first quarter of 2025. The following indicators were assessed: the proportion of IS patients admitted to stroke units within the first 4.5 hours after symptom onset; the frequency of intravenous thrombolytic therapy (IVT), including the use of telemedicine technologies for IVT administration; the rate of thrombectomy; and the hospital mortality rate from IS. The implementation of telemedicine technologies was evaluated based on the presence of teleconsulted primary stroke units (tele-PSUs) in each region and the proportion of IS patients who received IVT in tele-PSUs. RESULTS: Over the study period, an increase was observed in the proportion of IS patients admitted within 4.5 hours of symptom onset - from 23% in 2015 to 31% in 2024. The frequency of IVT and thrombectomy procedures increased from 2% and 0.1% in 2015 to 10.2% and 2.8% in the first quarter of 2025, respectively. The rate of IVT performed in tele-PSUs in 2024 was 13.6% of IS patients admitted to tele-PSUs. The ischemic stroke mortality rate decreased from 16.8% in 2015 to 12.6% in the first quarter of 2025. CONCLUSIONS: Reperfusion therapy technologies for IS are being actively implemented across the country, significantly improving clinical approaches to stroke management. Increasing the number of reperfusion procedures will contribute to improved patient outcomes, reduced mortality, and increase the number of individuals achieving favorable recovery of neurological functions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".