Predictors of the unfavorable outcomes in acute ischemic stroke patients treated with alteplase, a multi-center randomized trial
Bibliographic record
Abstract
Worldwide, stroke is a leading cause of long-term disability in adults. Alteplase is the only approved treatment for acute ischemic stroke (AIS) and results in an improvement in a third of treated patients. We evaluated the post-stroke unfavourable outcome predictors in alteplase-treated patients from Egypt and Saudi Arabia. We assessed the effect of different risk factors on AIS outcomes after alteplase in Egypt and Saudi Arabia. Our study included 592 AIS alteplase-treated patients. The relationship between risk factors, clinical presentation, and imaging features was evaluated to predict factors associated with poor outcomes. An mRS score of three or more was used to define poor outcomes. Poor outcome was seen in 136 patients (23%), and Patients with unfavourable effects had significantly higher admission hyperglycaemia, a higher percentage of diabetes mellitus, cardioembolic stroke, and a lower percentage of small vessel stroke. Patients with higher baseline NIHSS score (OR 1.39; 95% CI 1.12-1.71; P = 0.003), admission hyperglycaemia (OR 13.12; 95% CI 3.37-51.1; P < 0.001), and post-alteplase intracerebral haemorrhage (OR 7.41; 95% CI 1.69-32.43; P = 0.008) independently predicted unfavourable outcomes at three months. In AIS patients treated with alteplase, similar to reports from other regions, in patients from Egypt and Saudi Arabia also reveal that higher NIHSS, higher serum blood sugar, and post-alteplase intracerebral haemorrhage were the predictors of unfavourable outcomes three months after ischemic stroke.Trial registration: (clinicaltrials.gov NCT06058884), retrospectively registered on 28/09/2023.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".