Abstract WP67: Utilization and Impact of Large Vessel Occlusion Stroke Screening Practices and Selected Target Stroke III Strategies in the Get-with-the-Guidelines Registry Hospitals
Bibliographic record
Abstract
Background: The utilization and impact of pre-hospital and in-hospital large vessel occlusion (LVO) stroke screening protocols and Target: Stroke phase III (TS III) best-practice strategies on time metrics for endovascular treatment at a national level, have not been studied. Methods: We sent an online survey in July 2022 to hospital representatives of 2528 hospitals participating in the GWTG registry about their EMS systems, clinical and imaging protocols, and utilization of selected TS III best-practice strategies (multiple choice, 0-100 scale, and yes/no questions). We obtained Individual patient-level data from the GWTG-Stroke Registry from January 2017 to March 2022. Multivariable linear regression models were performed to investigate the associations of these strategies with door-to-puncture (DTP) in endovascular (EVT) patients. Results: Out of 2455 sites that met our inclusion criteria, 1455 sites completed the survey, with a response rate of 59.3%. Hospital-level baseline characteristics, utilization of selected LVO screening practices, and site-reported strategies associated with shorter DTP times are shown (Table 1). Strategies associated with shorter DTP were the performance of simultaneous vascular imaging along with non-contrast CT scan on all stroke patients within 24 hours, and the use of newer technologies for LVO detection in the field leading to a 7.1 min (CI -12.8, -1.5) decrease and a 10 min (CI -18.6, -1.3) decrease in DTP with every 25% increase in the utilization of these strategies, respectively (Table 1). Conclusion: The simultaneous performance of vascular imaging with non-contrast CT scan in all stroke patients presenting within 24 hours and the use of newer technologies for LVO detection in the field could lead to shorter DTP times.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".