Abstract 186: IV tPA Recanalization Rates by Site of Occlusion and Time After tPA Bolus- Main Results Of The Interrsect Multinational Multicenter Prospective Cohort Study
Bibliographic record
Abstract
Introduction: Decisions to transport patients from primary to comprehensive stroke centre would be influenced by info on likelihood/timing of spontaneous or IV tPA recanalization (recan). We examined recan rates by time for a wide range of occlusion sites in the INTERRSeCT multicenter prospective cohort study. Methods: Acute stroke patients consented/enrolled at 12 centers/5 countries if intracranial occlusion present on baseline CTA; eGFR≥60 ml/min. CTA was repeated 2-6 hrs later for recan unless patient taken for EVT (first run of angio used instead). Primary outcome was successful recan (rAOL scale score 2b/3) interpreted by central core lab. Results: 619 patients enrolled, 81.6% received IV tPA. 59.9% recan by follow-up CTA and 40.1% by first run angio. Median baseline NIHSS 14 (IQR 11); mean age 70.1 yrs (SD 13); median onset to baseline CTA 115 mins (IQR 108). Recan assessment imaging median 162 mins (IQR 198) from IV tPA bolus or baseline CTA (if no IV tPA). Successful recan (rAOL 2b-3) rates comparing baseline to repeat imaging shown in Figure 1a (IV tPA red; no IV tPA blue). IV tPA had much higher recan than non-IV tPA group (30.5% vs 11%, p<0.0001). Successful recan rates by occlusion site and by time from IV tPA bolus shown in Figure 1b. Site of occlusion, tPA administration, time from tPA to recan assessment and baseline residual flow were the only independent predictors of recan (all p<0.0001). Distal M1 MCA had highest recan [RR 4.12; 95% CI 1.91-8.86 vs. ICA]. Conclusions: Early recan rates were low across all occlusion sites. Beyond 6 hrs post tPA, recan rates approached EVT levels except for ICA. Imaging factors such as residual flow may further refine transport/triage decisions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".