Abstract W P36: Timing Of Collateral Circulation In Acute Stroke And Its Effect On Predicting Outcomes
Bibliographic record
Abstract
Background: We previously reported that Miteff scoring system on pre-tPA CTA was able to predict good functional outcomes in AIS, conversely substantial collateral recruitment on the 24hr CTA led to worse outcomes. We now aimed to determine if the degree of collateral circulation at a specific timing affects its ability to prognosticate outcomes. Methods: Patients treated with IV-tPA during 2018-2012 were included. Patients were stratified by onset-to-CTA timing from 0-60, 61-120, 121-180 and >180 minutes. Two independent neuroradiologists evaluated intracranial collaterals using the Miteff’s system, Maas system, and Alberta Stroke Program Early CT score (ASPECTS) 20-point methodology. Good and severely poor outcomes at 3-months were defined by modified Rankin Scale (mRS) score of 0-1 points and 5-6 points, respectively. SICH was intracranial bleed with NIHSS increase of ≥4 points. Results: 250 Consecutive AAIS patients were included. 52 patients in the 0-60 group, 89 in the 61-120 group, 59 in the 121-180 group and 40 patients in the >180 group. On multivariate analysis from 0-120 minutes, good collaterals by Miteff classification showed a trend to good outcomes (OR 2.460 95%CI 0.985- 9.770, p =0.06) and significantly prevented severely poor outcomes (OR 0.199 95% CI 0.053-0.743, p=0.016) and SICH (OR 0.196 95%CI 0.045-0.862, p=0.03). However the association with prevention of unfavourable outcomes was no longer present in the 121 -180 and > 180 minutes groups. Conclusions: Good collaterals are associated with better functional outcomes however this benefit may be time dependent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".