Assessing Reperfusion and Recanalization as Markers of Clinical Outcomes After Intravenous Thrombolysis in the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET)
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Reperfusion and recanalization have both been used as surrogate markers of clinical outcome in trials of stroke thrombolysis. We aimed to prove that the beneficial impact of recanalization with intravenous tissue plasminogen activator on clinical outcomes is attributable to reperfusion in the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET). METHODS: EPITHET was a prospective, randomized, placebo-controlled trial of intravenous tissue plasminogen activator in the 3- to 6-hour window. Reperfusion was defined as >90% reduction in magnetic resonance perfusion-weighted imaging lesion volume and recanalization as improvement of MR angiographic Thrombolysis In Myocardial Infarction grading by > or =2 points from baseline to Day 3 to 5. RESULTS: At Day 3 to 5, reperfusion and recanalization with intravenous tissue plasminogen activator were strongly correlated. Reperfusion was associated with improved clinical outcome independent of whether recanalization occurred. In contrast, recanalization was not associated with clinical outcome when reperfusion was included as a covariate in regression analyses. CONCLUSIONS: Reperfusion is a surrogate marker of clinical outcomes independent of recanalization based on the criteria applied in EPITHET. The impact of recanalization on clinical outcomes was attributable to reperfusion.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".