Association Between Emergency Medical Services Intervention Volume and Out-of-Hospital Cardiac Arrest Survival: A Propensity Score Matching Analysis
Bibliographic record
Abstract
BACKGROUND: Out of hospital cardiac arrest (OHCA) survival rates are very low. An association between institutional OHCA case volume and patient outcomes has been documented. However, whether this applies to prehospital emergency medicine services (EMS) is unknown. OBJECTIVES: To investigate the association between the volume of interventions by mobile intensive care units (MICU) and outcomes of patients experiencing an OHCA. METHODS: A retrospective cohort study including adult patients with OHCA managed by medical EMS in five French centers between 2013 and 2020. Two groups were defined depending on the overall annual numbers of MICU interventions: low and high-volume MICU. Primary endpoint was 30-day survival. Secondary endpoints were prehospital return of spontaneous circulation (ROSC), ROSC at hospital admission and favorable neurological outcome. Patients were matched 1:1 using a propensity score. Conditional logistic regression was then used. RESULTS: 2,014 adult patients (69% male, median age 68 [57-79] years) were analyzed, 50.5% (n = 1,017) were managed by low-volume MICU and 49.5% (n = 997) by high-volume MICU. Survival on day 30 was 3.6% in the low-volume group compared to 5.1% in the high-volume group. There was no significant association between MICU volume of intervention and survival on day 30 (OR = 0.92, 95%CI [0.55;1.53]), prehospital ROSC (OR = 1.01[0.78;1.3]), ROSC at hospital admission (OR = 0.92 [0.69;1.21]), or favorable neurologic prognosis on day 30 (OR = 0.92 [0.53;1.62]).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".