Intravenous Thrombolytic Therapy for Acute Ischemic Stroke: Experiences of a Tertiary Hospital in Turkey
Bibliographic record
Abstract
Objective: The aim of this study is to retrospectively collect the data of 95 patients with ischemic stroke who received intravenous (IV) tissue plasminogen activator (tPA) therapy between 2015 and 2019 in our clinic, to present the experiences of our clinic. Method: The data of the patients, who applied to our clinic with the diagnosis of acute ischemic stroke between 2015 and 2019, who received thrombolytic therapy, were reviewed retrospectively. General socio-demographic data, clinical features, National Institute Health Scale Scores (NIHSS), Alberta Stroke Program Early Computed Tomography (ASPECT) scores, symptom-to-door, door-to-needle, and symptom-to-needle times, treatment-related complications and Modified Rankin Scores (mRS) at the third month after treatment were evaluated. Results: Procedure-related major or minor bleeding complications developed in 18% of our patients who received tPA therapy. The mortality rate was 15% and 6.3% of these were caused by cerebral hemorrhage due to treatment complications and 8.7% were due to other systemic complications. Logistic regression analysis revealed that only the ASPECT score from factors that we found to be effective on the risk of developing complications was a highly effective factor in the development of complications (p=0.034, OR: 2.131). Conclusions: This study has shown that our results are generally compatible with the literature. Although there is an increase in the number of patients receiving tPA in our clinic every year, we aim to increase awareness of this therapy clinically and to expand the use of IV thrombolytic therapy in selected patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".