Abstract TP359: Partnering in the Community to Improve Long Term Stroke Outcomes
Bibliographic record
Abstract
Background: Stroke mortality has fallen from the third leading cause of death to the fifth; it remains the leading cause of disability. Providers know the appropriate standard of care to preserve life and reduce disability, but treatment is time limited. Patients who present with stroke signs and symptoms (S/S) outside the window for treatment are at risk for lifelong disability. We united with our Faith Community Partnership program (FCPP) to educate local church congregation members about the S/S of stroke. Purpose: The aim of our project was to bring community members with stroke symptoms urgently into the emergency care setting so that disability reducing treatments could be rendered. Methods: A quality improvement (QI) project was designed using the FCPP to deliver education to members within 32 church communities. Four FCPP nurses along with parish community volunteers distributed FAST cards with stroke S/S at health events starting the second quarter of 2014. The FCPP held at least six events per month; they were attended by 50-100 guests. FCPP workers highlighted the importance of prompt recognition of symptoms as well as emergent activation of Emergency Medical Services. Results: The Get with the Guideline’s Registry was accessed; ten quarters of data were reviewed to compare symptom onset to time of arrival at our hospital versus all certificated TJC/AHA/ PSC hospitals. An unpaired two tailed t test comparing patients arriving in less than one hour from symptom onset was extremely statistically significant in favor of the QI group, P < 0.0001. Equally statistically significant (p<0.0001) were patients arriving in less than 240 minutes from symptom onset compared to those arriving after 240 minutes. Patient arrival from 61-240 minutes was not quite statistically significant, =0.0746. Conclusion: Stroke education within the community affords providers better opportunity to deliver disability reducing treatments.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".