Intra-arterial thrombolysis in acute ischemic stroke: a review of pharmacologic approaches
Bibliographic record
Abstract
Ischemic stroke is a major public health problem worldwide. The potential to cure stroke patients with intravenous thrombolytic therapy has evolved to the use of intra-arterial thrombolytic agents. Fewer than 200 patients have been enrolled in randomized trials of intra-arterial therapy. In this article the authors have reviewed the literature listed in MEDLINE and EMBase, and searched relevant articles to examine the role of fibrinolytic agents in acute interventional stroke therapy. Only English language articles reporting five or more patients were included. Outcomes were defined at 90 days. Good outcome was defined on the modified Rankin Scale. Symtpomatic hemorrhage was defined as hemorrhage in the setting of clinical deterioration in the first 24 to 48 h. The search identified 57 studies of which 44 reported usable data. Only three randomized trials were reported. Of a total of 1140 patients, most (73%) were treated open-label with urokinase (Abbokinase, Abbott Laboratories). The best outcomes were reported in case series and slightly worse outcomes were reported in clinical trials. Overall, it was not possible to distinguish whether one agent was superior to the others. There is a paucity of published evidence on intra-arterial therapy for acute ischemic stroke. Alteplase (Activase, Genentech Inc.) is currently the drug of choice simply because it is available and it is the current intravenous standard. Further trials and developments are anticipated.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".