Streamlining door to recanalization processes in endovascular stroke therapy
Bibliographic record
Abstract
BACKGROUND: In acute stroke due to large vessel occlusion, faster reperfusion leads to better outcomes. We analyzed the effect of optimization steps aimed to reduce treatment delays at our center. METHODS: Consecutive patients with ischemic stroke treated with endovascular therapy were prospectively analyzed. We divided the patients into pre-optimization (20 April 2012 to 8 October 2013) and post-optimization (9 October 2013 to 29 July 2014) periods. The main interventions included: (1) continuous feedback; (2) standardized immediate emergency department attending to stroke attending communication with interventional team activation for all potential interventions; (3) pre-notification by the emergency medical service; (4) minimizing additional diagnostic testing; (5) direct transport to the CT scanner; (6) transport directly from the CT scanner to the angiography suite. The main metric used to measure improvement was door to groin puncture time (D2P). RESULTS: We included a total of 286 patients (178 pre-optimization, 108 post-optimization). There were no significant differences between major baseline characteristics between the groups with the exception of higher median CT Alberta Stroke Program Early CT Score in the pre-optimization group (p=0.01). Median D2P improved from 105 min pre-optimization to 67 min post-optimization (p=0.0002). Rates of good clinical outcomes (modified Rankin Scale 0-2 at 3 months) were similar in both groups, with a trend toward a better outcome in the post-optimization group in a subgroup analysis of patients with anterior circulation occlusion who received intravenous tissue plasminogen activator. CONCLUSIONS: This pilot study demonstrates that D2P times can be significantly reduced with a standardized multidisciplinary approach. There was no significant difference in the rate of 3-month good outcome, which is most likely due to the small sample size and confounding baseline patient characteristics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".