Locations of Cardiac Arrest: Affirmation for Community Public Access Defibrillation (PAD) Program
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to describe the regional locations of cardiac arrest, and to identify public locations and the annual incidence of arrests within the identified locations, in order to help to determine optimal placement of Automatic External Defibrillators (AEDs) under the regional Public Access Defibrillation (PAD) Program. This is a retrospective study. METHODS: The locations of cardiac arrest were abstracted from Ambulance Call Reports (ACRs) collected by the Essex-Kent Base Hospital Centre from regional ambulance services throughout the City of Windsor, and the Counties of Essex and Kent, Ontario, Canada, from 01 January 1994 through 31 December 2000. Arrest locations were grouped into five categories, and then the number of public venues was determined. Public Sites were grouped into 28 Public Locations. Also included in the Public Sites were both General Industry and Outdoors categories. Categories identified but excluded from Public Sites were Institutions and Private Residences. RESULTS: During the study, 2,295 arrests occurred, 152 cases were excluded, 2,142 arrests were categorized, (average annual incidence of 306 +/- 50.4 cardiac arrests), 329 (15.4%) of which were in Public Sites. Nineteen public venues had an average of > 1 arrest/year, and nine public venues had an average of < or = 1 arrest/year during the study, period. Calculations of the annual incidence of arrests for each public location were completed. CONCLUSIONS: These findings have significant prehospital emergency cardiac care implications for communities that wish to strengthen/improve their responses to out-of-hospital cardiac arrests. Public Access Defibrillation Programs should identify the site-specific incidence of arrest within their communities in order to provide legitimacy for funding and planning of programs. Training and availability of AEDs will reduce the time to first shock, thus strengthening the chain-of-survival and will save more lives.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".