Design projections for prospective studies evaluating the clinical effectiveness of cardiac arrest detection technologies
Bibliographic record
Abstract
BACKGROUND: Recent developments have illustrated the feasibility of scalable out-of hospital cardiac arrest (OHCA) detection using consumer-wearable products. While initial evidence is promising, the real-world effectiveness of these technologies is unproven and design decisions to minimize false positives may constrain clinical impact. To support the real-world evaluation of these technologies, we analyzed registry data and evaluated strategies for a clinical trial. METHODS: We analyzed Emergency Medical Services (EMS)-treated and EMS-untreated cases in the British Columbia Cardiac Arrest Registry (2019-2020) who may have benefitted from cardiac arrest detection. Within strata formed by Age (0-35, 36-55, 56-65, 65+) and Sex, we estimated strata-specific treatment effects from recognition and calculated the required sample sizes for a set of potential primary clinical outcomes for a trial (survival to hospital discharge, initial shockable cardiac rhythm, EMS treatment). We compared this with population incidence to inform prospective recruitment requirements. RESULTS: Within a population-at-risk of 9,739,356, 11,382 sentinel cases met criteria for inclusion. Strata-specific incidence ranged from 18.3 per 100,000 to 395 per 100,000. The most statistically efficient subgroup for a survival outcome was Males aged 56-65 (171,676 needed, total). For a trial outcome of EMS treatment or initial shockable rhythm, Males over 65 were the most efficient subgroup within which to test the intervention (7,608 and 66,962 needed, total respectively). CONCLUSION: A prospective trial of OHCA detection technologies will require large recruitment targets. Subgroups with the highest estimated treatment effect are not those with the highest incidence. Large trials in the general population will require multiple centers and innovative recruitment strategies that may be aided by partnership with industry.
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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 | 0.266 | 0.322 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".