Onset to Reperfusion Times Strongly Determine Clinical Outcome Across a Wide Range of ASPECTS Scores in Endovascular Ischemic Stroke Therapy (I6.0010)
Bibliographic record
Abstract
OBJECTIVE: To investigate whether established infarct core size on baseline imaging modifies the relation of onset to reperfusion time to functional outcome in acute ischemic stroke patients treated with mechanical thrombectomy. BACKGROUND: Both onset to reperfusion (OTR) time and extent of established infarction on pretreatment imaging, indexed by the Alberta Stroke Program Early CT Scale (ASPECTS), are important outcome determinants in acute ischemic stroke. Their potential interaction has not been well delineated. DESIGN/METHODS: We analyzed the Triple-S database, comprised of individual patient data pooled from 3 prospective Solitaire stent retriever trials and registries (SWIFT, SWIFT PRIME, STAR). Inclusion criteria were: treatment with a Solitaire device and achievement of substantial reperfusion (TICI 2b-3). Three month outcomes analyzed included functional independence (mRS 0-2) and freedom from disability (mRS 0-1). RESULTS: Among 305 patients, mean age was 66.9±12.6 years, 58[percnt] female, NIHSS 16.9±4.6. Baseline ASPECTS scores were 5-6 in 7.6[percnt], 7-8 in 37.1[percnt], and 9-10 in 52.0[percnt], and onset to reperfusion time mean 297±95 min. At 3 months, nondisabled outcome (mRS 0-1) was achieved in 43.9[percnt] and functional independence (mRS 0-2) in 60.1[percnt]. For mRS 0-2 outcomes, between OTR times of 2 to 8 hours, rates of good outcome at all timepoints were higher with higher ASPECTS scores, but declined with similar steepness. For ASPECTS 9-10, good outcome rates were 85.6[percnt] with OTR of 2-3h versus 41.1[percnt] with OTR of 7-8h. OTR times associated with 50[percnt] of patients achieving mRS 0-2 were: 390m for ASPECTS 9-10, 330m for 7-8, and 260m for 6 or less. CONCLUSIONS: Patients with higher ASPECTS scores are more likely to have good clinical outcomes at all onset to reperfusion intervals. But from 2 hours on, benefit falls at a steep and even pace across all ASPECT categories, reinforcing the need to treat all patients as fast 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".