Abstract 191: Community Uptake of a Smartphone Application to Recruit Bystander Basic Life Support for Victims of Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Background: PulsePoint is a smartphone application that links bystanders with the 911 response to cardiac arrest. The application facilitates bystander basic life support while EMS personnel are en route. After community members download the free software to their smartphones, they can receive automated alerts directing them to nearby public location out-of-hospital cardiac arrests and public AED locations. Objective: To quantify the uptake of the PulsePoint application in two Californian communities. Methods: PulsePoint was launched in San Ramon December 2011 and in San Jose February 2012. Promotional efforts in both communities included mass media campaigns, public service announcements in theatres and the use of social media outlets (e.g. Twitter and Facebook). Data from iTunes, Android Market and the PulsePoint enterprise server were collected to document uptake. Results: As of May 27, 2012 there were 23,069 PulsePoint application downloads (18,000 iOS, 5,069 Android). Figure 1 shows the cumulative installations of the PulsePoint application over time. The number of devices registered on the PulsePoint server is 11,167, accounting for users who removed the application from their device after installation. Of those devices registered, only 30% (3,350/11,167) have settings configured to receive cardiac arrest alerts. To date, there have been 64 suspected cardiac arrest incidents associated with a PulsePoint alert. Conclusion: There has been significant uptake of the PulsePoint application in San Ramon and San Jose with an increasing number of users over time. Future work should focus on methods to maximize the number of users configuring the cardiac arrest alerts feature to be active, to encourage retention of the application and determining the effectiveness of the application with respect to increasing bystander CPR, AED use and survival for out-of-hospital cardiac arrest victims in the community.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".