Abstract TP18: First-Pass Effect May Reduce the Impact of Delays to Treatment in Endovascular Thrombectomy: Analysis of the STRATIS Registry
Bibliographic record
Abstract
Introduction: First-pass reperfusion effect (FPE) appears superior to multiple device passes in achieving good functional recovery in endovascular thrombectomy (EVT). It is unclear if this represents an epiphenomenon or a true independent effect. Historically, earlier treatment has been associated with improved functional recovery. We analyzed how these two variables interact using the STRATIS registry data. Methods: The STRATIS registry prospectively enrolled large vessel occlusion, stroke patients, treated with Solitaire and/or Mindframe Capture low profile revascularization devices within 8 hours of symptom onset. Reperfusion was assessed by an independent core lab. Results: A total of 984 patients were enrolled (mean age 67.8 +/- 14.7 years, 54.2% male, median NIHSS 17). Mean time from stroke onset to groin puncture was 226.4+/- 100.0 minutes. At 90 days, functional recovery (mRS 0-2) was achieved in 56.5%. Core lab assessment was performed in 824 cases with a mTICI2b/3 rate of 87.9%. Every 60-minute delay to treatment was associated with less functional recovery cOR 0.79 (95% CI, 0.68 - 0.93). In patients with first-pass effect reperfusion (FPE), delay to treatment did not affect functional recovery FPE-mTICI 2b cOR 1.03 (95% CI, 0.83 - 1.28) or FPE-mTICI 2c/3 cOR 0.96 (95% CI, 0.84 - 1.11). Poor reperfusion (FPE-mTICI <2b) maintained a negative relationship between functional recovery and delay to treatment cOR 0.76 (95% CI, 0.66 - 0.88). Conclusion: First pass effect may reduce the impact of delays to treatment compared to historical data. Further studies to determine the mechanism of this effect are required.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".