Achieving Faster Recanalization Times by IA Thrombolysis in Acute Ischemic Stroke: Where Should We Direct Our Efforts?
Bibliographic record
Abstract
Faster recanalization correlates with better outcomes in acute ischemic stroke. We analyzed times from arrival in ER to end of treatment in patients undergoing endovascular treatment for acute ischemic stroke at our institution.We retrospectively studied patients who underwent IA procedures for stroke from 2005 to 2009 noting the times of arrival to ER, CT scan, arrival to DSA, arterial puncture and recanalization from our endovascular database. A subgroup analysis was performed based on administration of GA, use of mechanical devices and whether the procedure was performed during regular hours or after hours.Of 101 patients, 53 were male, with a median age of 66 years (range 18-87). There were 81 anterior circulation strokes. Median ER to CT time was 22 min (2-1025), CT to DSA arrival time 80 min (range 4-990), DSA arrival to puncture time 24 min (range 0-75) and puncture to recanalization time 84 min (range 11-206). 23.3% of patients had an ER to CT time interval of > 60 min and 71.3 % had a CT to DSA time interval of > 60 min contributing to significant in-hospital delays. For subgroup analysis the Mann-Whitney test was used. No significant differences in CT to DSA arrival (p=0.8), DSA arrival to puncture (p=0.1) and puncture to recanalization (p=0.59) times were noted between patients with and without GA. No significant difference was noted in puncture to recanalization times with or without device (p=0.78). 39 cases were done during regular (R) hours and 62 after (A) hours. Median ER to CT time (R=18 min, A = 27 min, p 0.02), CT to DSA arrival time (R=64 min, A=90 min, p 0.004) and DSA arrival to puncture time (R=18 min, A=25 min, p 0.003) was significantly higher after hours.ER to CT and CT to DSA arrival times in patients undergoing endovascular stroke therapy show wide variability and therefore, considerable scope for reduction. Time differences during regular and after hours should serve as a reminder to make efforts to reduce overall ischemic times in spite of staffing patterns and resource availability.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".