Out-of-hospital cardiac arrests occurring in southern Ontario health care clinics
Bibliographic record
Abstract
OBJECTIVE To determine the proportion of public-location out-of-hospital cardiac arrests (OHCAs) that occur in health care clinics and to describe bystander cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) use during these episodes. DESIGN Our study was a retrospective cohort study of 679 nontraumatic OHCAs recorded in the Resuscitation Outcomes Consortium Epistry–Cardiac Arrest database. SETTING Out-of-hospital medical clinics and other public locations in Toronto, Ont, and the surrounding municipal regions of Hamilton, Durham, York, Peel, Simcoe, and Muskoka. PARTICIPANTS A total of 679 consecutive patients suffering nontraumatic OHCAs of presumed cardiac cause in public locations. MAIN OUTCOME MEASURES The proportion of public-location cardiac arrests occurring in medical clinics and the occurrence of bystander CPR and bystander use of AEDs. RESULTS Twenty-two of the 679 public-location cardiac arrests occurred in health care clinics (3.2%, 95% confidence interval 1.9% to 4.6%). Bystander CPR occurred more often in health care clinics (73% of episodes in clinics compared with 46% in other public places, P = .02), but there was no statistically significant difference in AED use between groups. Twenty-seven percent of those suffering cardiac arrests in health care clinics did not receive any bystander CPR, and more than 90% did not have AEDs applied. CONCLUSION Although the response to cardiac arrest in out-of-hospital medical clinics is superior to the response to those arrests that occur in other public settings, it remains suboptimal. Increasing CPR training among staff and improving access to AEDs in medical clinics might improve the response to OHCA in medical clinics and ultimately improve outcomes for patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".