Stroke Treatment Delay Limits Outcome After Mechanical Thrombectomy: Stratification by Arrival Time and ASPECTS
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Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Mechanical thrombectomy (MT) has helped many patients achieve functional independence. The effect of time-to-treatment based in specific epochs and as related to Alberta Stroke Program Early CT Score (ASPECTS) has not been established. The goal of the study was to evaluate the association between last known normal (LKN)-to-puncture time and good functional outcome. METHODS: We conducted a retrospective cohort study of prospectively collected acute ischemic stroke patients undergoing MT for large vessel occlusion. We used binary logistic regression models adjusted for age, Modified Treatment in Cerebral Ischemia score, initial National Institutes of Health Stroke Scale, and noncontrast CT ASPECTS to assess the association between LKN-to-puncture time and favorable outcome defined as Modified Rankin Score 0-2 on discharge. RESULTS: Among 421 patients, 328 were included in analysis. Increased LKN-to-puncture time was associated with decreased probability of good functional outcome (adjusted odds ratio [aOR] ratio per 15-minute delay = .98; 95% confidence interval [CI], .97-.99; P = .001). This was especially true when LKN-puncture time was 0-6 hours (aOR per 15-minute delay = .94; 95% CI, .89-.99; P = .05) or ASPECTS 8-10 (aOR = .98; 95% CI, .97-.99; P = .002) as opposed to when LKN-puncture time was 6-24 hours (aOR per 15-minute delay = .99; 95% CI, .97-1.00; P = .16) and ASPECTS <8 (aOR = .98; 95% CI, .93-1.03; P = .37). CONCLUSION: Decreased LKN-groin puncture time improves outcome particularly in those with good ASPECTS presenting within 6 hours. Strategies to decrease reperfusion times should be investigated, particularly in those in the early time window and with good ASPECTS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it