Feasibility of public CPR training kiosks to increase bystander resuscitation: a Monte Carlo simulation study
Bibliographic record
Abstract
Background Survival after out-of-hospital cardiac arrest (OHCA) depends on immediate bystander cardiopulmonary resuscitation (CPR), yet rates range from 42-70% in Canada. Traditional CPR education faces barriers of access, retention, and scalability. Public CPR kiosks are a novel alternative, but their potential population-level impact is uncertain. Methods We developed a Monte Carlo and queueing-based simulation model to estimate the effect of CPR kiosks on bystander CPR in Toronto, Canada. The model incorporated venue-specific passer volumes, funnel attrition (approach, engagement, practice, competence), demographic witness likelihood, post-training willingness to act, and skill retention. Outcomes included competent trainees, witness-weighted trainees, additional CPR attempts, change in citywide bystander CPR, lives saved, and cost-effectiveness. We modelled deployment of 30 kiosks across four venue types—mega-volume public spaces (n=6), hospitals (n=8), large commercial venues (n=8), and community sites (n=8)—each with empirically informed passer volumes and engagement probabilities. Results Median annual throughput per kiosk ranged from 488 competent trainees (95% credible interval [CrI], 94–1550) at small sites (0.5 million passers) to 19,618 (95% CrI, 3706–50,000) at mega-sites (40 million passers). Witness-weighted trainees were highest in hospitals and pharmacies, reflecting more caregivers and seniors. Training increased willingness to act from 40% to 60–80%; this action uplift strongly influenced outcomes. In Toronto, a blended network of 30 kiosks (6 mega, 8 hospital, 8 community, 8 large) increased bystander CPR by 7.5–8.0 percentage points, with a 90–95% probability of meeting or exceeding a 5–point target within one year. This translated to ∼150 additional CPR attempts and 15 lives saved annually. Costs were ∼$10,000 per life saved and ∼$1250 per quality-adjusted life year (QALY). Conclusions Simulation modeling suggests CPR kiosks can feasibly and cost-effectively increase bystander CPR, with impact shaped by visibility, action willingness, and targeting individuals most likely to witness arrest.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".