A traumatic tale of two cities: does EMS level of care and transportation model affect survival in patients with trauma at level 1 trauma centres in two neighbouring Canadian provinces?
Bibliographic record
Abstract
BACKGROUND: Two distinct Emergency Medical Services (EMS) systems exist in Atlantic Canada. Nova Scotia operates an Advanced Emergency Medical System (AEMS) and New Brunswick operates a Basic Emergency Medical System (BEMS). We sought to determine if survival rates differed between the two systems. METHODS: This study examined patients with trauma who were transported directly to a level 1 trauma centre in New Brunswick or Nova Scotia between 1 April 2011 and 31 March 2013. Data were extracted from the respective provincial trauma registries; the lowest common Injury Severity Score (ISS) collected by both registries was ISS≥13. Survival to hospital and survival to discharge or 30 days were the primary endpoints. A separate analysis was performed on severely injured patients. Hypothesis testing was conducted using Fisher's exact test and the Student's t-test. RESULTS: 101 cases met inclusion criteria in New Brunswick and were compared with 251 cases in Nova Scotia. Overall mortality was low with 93% of patients surviving to hospital and 80% of patients surviving to discharge or 30 days. There was no difference in survival to hospital between the AEMS (232/251, 92%) and BEMS (97/101, 96%; OR 1.98, 95% CI 0.66 to 5.99; p=0.34) groups. Furthermore, when comparing patients with more severe injuries (ISS>24) there was no significant difference in survival (71/80, 89% vs 31/33, 94%; OR 1.96, 95% CI 0.40 to 9.63; p=0.50). CONCLUSION: Overall survival to hospital was the same between advanced and basic Canadian EMS systems. As numbers included are low, individual case benefit cannot be excluded.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".