The Impact of Time to Reperfusion on Recanalization Rates and Outcome After Mechanical Thrombectomy
Bibliographic record
Abstract
Background: Timely and effective recanalization to salvage the penumbra is the main determinant of outcome in acute ischemic strokes. Randomized controlled trials on late window mechanical thrombectomy (MT) have proved its safety and efficacy upto 24 h after stroke onset. We looked at the impact of time to reperfusion on vessel recanalization rates and short-term outcome in patients undergoing MT for large vessel occlusion. Methods: The clinical, imaging, and outcome of all patients undergoing MT upto 24 h from last seen normal was extracted from a prospectively maintained ischemic stroke database from January 2012 till September 2019. Results: There were 145 patients with a mean (SD) age of 58.2 (±14) years. Of them, 28 had wake up/unknown time of onset stroke and 9 presented beyond >360 min. There were 23 vertebrobasilar strokes. Median National Institute of Health Stroke scale score (NIHSS) at admission was 16.4 (Inter quartile range (IQR) 12–21). CT-Alberta Stroke program early CT score (CT-ASPECTS) was excellent (8–10) in 39 (31.6%) and fair (5–7) in 77 (63.6%) patients in anterior circulation strokes. About 25% underwent bridging therapy. Recanalization rates did not differ between those presenting early (<6 h) versus wake up strokes and late presenting patients (81.79% vs 71.9%). Symptomatic Intracerebral hemorrhage (ICH) occurred in 5%. At 3 months, excellent outcome (modified rankin scale <2) was observed in 28.9%. While Admission NIHSS remained strong predictor of poor outcome at 3 months, delay in presentation did not impact MT outcome (37.5% vs 45.79% and P = 0.460). Conclusions: The recanalization rates were similar in patients irrespective of the time to reperfusion from stroke onset. The functional outcome was not inferior in late presenters selected by advanced imaging.
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".