Abstract TP467: Prioritize- Ensure the Health of Your Community
Bibliographic record
Abstract
Background: As a rural stroke program in an aging community, we learned that more of our community members were choosing to drive to the hospital rather than utilizing EMS services when experiencing stroke symptoms. In collaboration with the Minnesota Department of Health we aimed to increase stroke awareness in our community, thus increasing EMS arrivals of patients with stroke symptoms. Methods: From June 2016 to June 2017, we participated in multiple community events. These included personal interaction with stroke-specific materials at three separate wellness events. We designed listener-specific radio PSA’s reaching over 30,000 listeners/hour on 6 local stations. We sent direct mailings via a postcard with a detachable magnet to 16,796 households. This highlighted stroke signs/symptoms, treatment options, and the importance of calling 9-1-1. We provided a stroke specific table service dinner with a presentation that was booked at max occupancy. Lastly, we utilized a “Mega-Brain” inflatable at an event with over 400 attendees and distributed stroke awareness materials. Results: Prior to the campaign from October 2015- June 2016 there were 140 stroke alerts (50%EMS vs 50% private vehicle, 25% stroke treated). The median last known well to arrival time was 121 [0,5760] minutes. During active participation from July 2016-June 2017, there were 151 stroke alerts (53% EMS vs 47% private vehicle arrivals, 21% strokes treated). The median last known well to arrival time was 115 [0,4320] minutes. At the conclusion from July 2017 – March 2018 there were 152 stroke alerts (53% EMS vs 47% private vehicle arrivals, 24% strokes treated). The median last known well to arrival time was 108 [1,10080] minutes. Conclusion: By identifying an educational gap and providing educational opportunities to the community, we experienced an increase in EMS activations for stroke symptoms as well as an unexpected increase in treated stroke cases. Educating the community on recognition of potential stroke symptoms, seeking early treatment, and utilization of EMS was an effective intervention in our service area. Continued community education is likely an important component of reducing death and disability from stroke by increasing early recognition and treatment of symptoms.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.088 | 0.028 |
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".