Stroke Treatment Delay Limits Outcome After Mechanical Thrombectomy: Stratification by Arrival Time and ASPECTS
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".