P.028 Tenecteplase for treatment of acute ischemic stroke in the extended time window, a review of current data
Bibliographic record
Abstract
Background: The use of Tenecteplase (TNK) in Extended Time Window (ETW) for Acute Ischemic Stroke (AIS) remains an ongoing debate. Methods: Systematic review of 3 Randomized controlled trials (RCTs)- TIMELESS, TRACE 3, CHABLIS-T II was conducted. Results: 1198 patients were enrolled: 603 received TNK, while 595 were controls. All 3 trials included patients with Internal Carotid and/ or Proximal Middle Cerebral Artery Occlusions; however, in TRACE 3, patients did not have access to endovascular thrombectomy. TIMELESS and CHABLIS-T II showed better recanalization in the TNK group but the median Modified Rankin Score was 3 at 90 days in both groups, demonstrating no benefit in clinical outcomes. Symptomatic Intracranial hemorrhage (sICH) was similar in the two groups. In TRACE 3, there was an improvement in functional outcomes at 90 days in the TNK group (33.0% vs. 24.2%), but the incidence of sICH was also higher (3.0% and 0.8%, respectively). Conclusions: Better recanalization rates are seen with TNK in ETW, but may not be associated with improved functional outcomes at 90 days compared to medical management. Incidence of sICH also remains largely favorable, except in TRACE 3, which showed a higher incidence in the TNK group. There remains a need for more RCTs in this population.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".