Onset to reperfusion time as a determinant of outcomes across a wide range of ASPECTS in endovascular thrombectomy: pooled analysis of the SWIFT, SWIFT PRIME, and STAR studies
Bibliographic record
Abstract
BACKGROUND: The time-benefit relationship of endovascular thrombectomy (EVT) according to the size of the core infarct has been incompletely explored in prior studies. We investigated whether established infarct core size on baseline imaging modifies the relationship between onset-to-reperfusion time (OTR) and functional outcomes in patients with acute ischemic stroke treated with EVT. METHODS: We analyzed a database containing individual patient data pooled from three prospective Solitaire stent retriever studies. The inclusion criteria were treatment with a Solitaire device and achievement of substantial reperfusion (modified Thrombolysis in Cerebral Infarction 2b-3). Main analyses were performed in patients with baseline Alberta Stroke Program Early CT Scores (ASPECTSs) of 7-10. RESULTS: Among the 305 patients (mean age 67±13 years, 58% women), the proportions of patients in different categories of pretreatment infarct extent were: small (ASPECTS 9-10) 52.0%, moderate (ASPECTS 7-8) 37.1%, and large (ASPECTS 0-6) 7.6%. The mean OTR was 297±95 min. At 3 months, 60.1% of the patients achieved a good outcome. For OTRs of 2-8 hours, the rates of good outcomes at all time points were higher with higher baseline ASPECTS but declined with similar steepness. Both baseline ASPECTS (OR 1.23 (95% CI 1.04 to 1.45)) and OTR (every 30 min delay, OR 0.80 (95% CI 0.73 to 0.88)) were independently associated with a good 3-month outcome. No interaction between OTR and baseline ASPECTS was observed. CONCLUSIONS: Although patients with higher baseline ASPECTS are more likely to have good clinical outcomes at all OTR intervals after 2 hours, this benefit consistently declines with time, even in patients with a small infarct core, reinforcing the need to treat all patients as quickly as possible.
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".