Abstract 62: A Statewide Cooperative Motivational Strategy Significantly Improves IV Alteplase Rates in Maryland: Golden Brain Award
Bibliographic record
Abstract
Introduction: The Maryland Stroke Coordinators Consortium and the Maryland Institute for Emergency Medical Services Systems, Health Care Facilities formed the Maryland Stroke Coalition (MSC) to improve stroke practices in Maryland. The aim of this performance improvement project was to determine if a motivational strategy improves thrombolytic metrics. Methods: In 2018 a stroke summit for Maryland stroke centers with expert faculty discussed thrombolytic best practices. Then MSC members met bimonthly to discuss how to implement AHA’s Target: Stroke Phase III. In October 2018 a motivational strategy was implemented to improve thrombolytic benchmarks. Quarterly, the stroke center with the fastest median door to needle time was awarded a Golden Brain trophy and a monetary award. After four quarters the stroke center with the fastest door to needle time will be recognized at a regional conference. Stroke coordinators voluntarily submitted quarterly data to the Chief of Special Programs, MIEMSS. Data submitted: quarterly rates IV Alteplase, median door to needle time, and % of IV Alteplase < 45 minutes. Results: Seventeen out of 39 possible stroke centers participated during the study period. Baseline data for the quarter prior to implementation revealed 84 pts received IV Alteplase. For the next three quarters IV Alteplase rates increased from baseline, respectively by 52% (n=128), 54% (n=129) and 65% ( n=139); and the median door to needle time was 48 minutes. The winning centers for each quarter reported median door to needle times < 30 minutes. From baseline (36%, 42/114) to quarter 3 there was a 15.5% (51.5%, =49/95) increase in patients being treated with IV Alteplase in < 45 minutes. Conclusion: Implementation of a motivational strategy and sharing best practices appears to be associated with increasing IV Alteplase administration volumes. The results of this PI project will be used to engage stakeholders to develop strategies to assist stroke centers remove barriers to improve door to needle times. The limitations of this project may be the small number of stroke centers participating and the effect of highly functioning centers participating.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".