P143 Comparative analysis of stroke care performane in West Georgia and West Ukraine
Bibliographic record
Abstract
<h3>Introduction</h3> West Georgia Medical Centre (WGMC) and the 1st Lviv Territorial Medical Union (1TMU) serve as the exclusive comprehensive stroke centers in West Georgia and West Ukraine, respectively. WGMC spans 31,500 km<sup>2</sup>, serving 950,000 residents in 23 cities/towns, while 1TMU extends over 21,833 km<sup>2</sup>, catering to 2,478,100 residents across 78 cities/towns. Despite distances of up to 220 km from WGMC and 138 km from 1TMU, patient transfer delays vary, highlighting their vital roles in stroke care. <h3>Aim of Study</h3> To evaluate the quality and performance of stroke services provided by these institutions. <h3>Methods</h3> Retrospective analysis was conducted on electronic medical records of patients undergoing mechanical thrombectomy (MT) at both institutions. Ukrainian data covers September 2022 to August 2023, while WGMC data spans from July 2019 to August 2023. Assessment parameters included the National Institutes of Health Stroke Scale (NIHSS), Alberta Stroke Program Early CT Score (ASPECTS), and Modified Rankin Scale (mRS). <h3>Results</h3> Enrollment comprised 72 1TMU and 65 WGMC patients. NIHSS scores, initial ASPECTS, and mRS upon discharge showed no significant differences. Symptom onset-to-admission time averaged 294 minutes at WGMC and 115 minutes at 1TMU. Sedation was prevalent in 91% of cases at 1TMU versus 41% at WGMC, with thrombolysis employed in 53% and 5% of cases, respectively. mTICI-3 reperfusion rates were 74% at WGMC and 85% at 1TMU. <h3>Conclusion</h3> Middle-income countries can deliver effective endovascular treatment for ischemic stroke with prompt patient arrival. However, substantial organizational and logistical enhancements within regional healthcare systems and hospitals are necessary to reduce delays and enhance workflow efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".