Door‐to‐Puncture: A Practical Metric for Capturing and Enhancing System Processes Associated With Endovascular Stroke Care, Preliminary Results From the Rapid Reperfusion Registry
Bibliographic record
Abstract
BACKGROUND: In 2011, the Brain Attack Coalition proposed door-to-treatment times of 2 hours as a benchmark for patients undergoing intra-arterial therapy (IAT). We designed the Rapid Reperfusion Registry to capture the percentage of stroke patients who meet the target and its impact on outcomes. METHODS AND RESULTS: This is a retrospective analysis of anterior circulation patients treated with IAT within 9 hours of symptom onset. Data was collected from December 31, 2011 to December 31, 2012 at 2 centers and from July 1, 2012 to December 31, 2012 at 7 centers. Short “Door-to-Puncture” (D2P) time was hypothesized to be associated with good patient outcomes. A total of 478 patients with a mean age of 68±14 years and median National Institutes of Health Stroke Scale (NIHSS) of 18 (IQR 14 to 21) were analyzed. The median times for IAT delivery were 234 minutes (IQR 163 to 304) for “last known normal-to-groin puncture” time (LKN-to-GP) and 112 minutes (IQR 68 to 176) for D2P time. The overall good outcome rate was 39.7% for the entire cohort. In a multivariable model adjusting for age, NIHSS, hypertension, diabetes, reperfusion status, and symptomatic hemorrhage, both short LKN-to-GP (OR 0.996; 95% CI [0.993 to 0.998]; P<0.001) and short D2P times (OR 0.993, 95% CI [0.990 to 0.996]; P<0.001) were associated with good outcomes. Only 52% of all patients in the registry achieved the targeted D2P time of 2 hours. CONCLUSIONS: The time interval of D2P presents a clinically relevant time frame by which system processes can be targeted to streamline the delivery of IAT care nationally. At present, there is much opportunity to enhance outcomes through reducing D2P.
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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 | 0.032 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".