Association between time to treatment and functional outcomes according to the Diffusion‐Weighted Imaging Alberta Stroke Program Early Computed Tomography Score in endovascular stroke therapy
Bibliographic record
Abstract
Background and purpose The rate at which the chance of a good outcome of endovascular stroke therapy (EVT) decays with time when eligible patients are selected by baseline diffusion‐weighted magnetic resonance imaging (DWI‐MRI) and whether ischaemic core size affects this rate remain to be investigated. Methods This study analyses a prospective multicentre registry of stroke patients treated with EVT based on pretreatment DWI‐MRI that was categorized into three groups: small [Diffusion‐Weighted Imaging Alberta Stroke Program Early Computed Tomography Score (DWI‐ASPECTS)] (8–10), moderate (5–7) and large (<5) cores. The main outcome was a good outcome at 90 days (modified Rankin Scale 0–2). The interaction between onset‐to‐groin puncture time (OTP) and DWI‐ASPECTS categories regarding functional outcomes was investigated. Results Ultimately, 985 patients (age 69 ± 11 years; male 55%) were analysed. Potential interaction effects between the DWI‐ASPECTS categories and OTP on a good outcome at 90 days were observed (Pinteraction = 0.06). Every 60‐min delay in OTP was associated with a 16% reduced likelihood of a good outcome at 90 days amongst patients with large cores, although no associations were observed amongst patients with small to moderate cores. Interestingly, the adjusted rates of a good outcome at 90 days steeply declined between 65 and 213 min of OTP and then remained smooth throughout 24 h of OTP (Pnonlinearity = 0.15). Conclusions Our study showed that the probability of a good outcome after EVT nonlinearly decreased, with a steeper decline at earlier OTP than at later OTP. Discrepant effects of OTP on functional outcomes by baseline DWI‐ASPECTS categories were observed. Thus, different strategies for EVT based on time and ischaemic core size are warranted.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".