Ambulance Density and Outcomes After Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
BACKGROUND: In out-of-hospital cardiac arrest (OHCA), geographic disparities in outcomes may reflect baseline variations in patients' characteristics but may also result from differences in the number of ambulances providing basic life support (BLS) and advanced life support (ALS). We aimed at assessing the association between allocated ambulance resources and outcomes in OHCA patients in a large urban community. METHODS: From May 2011 to January 2016, we analyzed a prospectively collected Utstein database for all OHCA adults. Cases were geocoded according to 19 neighborhoods and the number of BLS (firefighters performing cardiopulmonary resuscitation and applying automated external defibrillator) and ALS ambulances (medicalized team providing advanced care such as drugs and endotracheal intubation) was collected. We assessed the respective associations of Utstein parameters, socioeconomic characteristics, and ambulance resources of these neighborhoods using a mixed-effect model with successful return of spontaneous circulation as the primary end point and survival at hospital discharge as a secondary end point. RESULTS: During the study period, 8754 nontraumatic OHCA occurred in the Greater Paris area. Overall return of spontaneous circulation rate was 3675 of 8754 (41.9%) and survival rate at hospital discharge was 788 of 8754 (9%), ranging from 33% to 51.1% and from 4.4% to 14.5% respectively, according to neighborhoods ( P<0.001). Patient and socio-demographic characteristics significantly differed between neighborhoods ( P for trend <0.001). After adjustment, a higher density of ambulances was associated with successful return of spontaneous circulation (respectively adjusted odds-ratio [aOR], 1.31 [1.14-1.51]; P<0.001 for ALS ambulances >1.5 per neighborhood and aOR, 1.21 [1.04-1.41]; P=0.01 for BLS ambulances >4 per neighborhood). Regarding survival at discharge, only the number of ALS ambulances >1.5 per neighborhood was significant (aOR, 1.30 [1.06-1.59] P=0.01). CONCLUSIONS: In this large urban population-based study of out-of-hospital cardiac arrests patients, we observed that allocated resources of emergency medical service are associated with outcome, suggesting that improving healthcare organization may attenuate disparities in prognosis.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".