In-Hospital Outcome Based on the Mode of EMS Transportation in a High-Income Rapidly Developing Middle Eastern Country
Bibliographic record
Abstract
BACKGROUND: Helicopter emergency medical services (HEMS) are considered as a standard component of advanced pre-hospital emergency care system. We assessed the clinical presentation and outcomes of trauma patients transported by HEMS versus ground emergency medical services (GEMS). METHODS: A retrospective analysis of prospectively collected trauma registry data at a level I trauma center was conducted for patients transported by GEMS and HEMS between 2011 and 2013. Patients’ data were categorized and analyzed based on the mode of transportation (MOT). RESULTS: A total of 4596 trauma patients were admitted to the hospital with a mean age of 31±15 years. Injured patients were transported to the trauma centre by GEMS (93.3%) and HEMS (6.7%). The common mechanisms of injury were motor vehicle crash (37%) and fall (25%). MVC victims were transported to the hospital by HEMS, whereas, patients with fall and pedestrian injuries (P=0.001 for all) were more likely to be transported by GEMS. Moreover, patients in the HEMS group had a higher frequency of head (p=0.001) and chest (p=0.001) injuries and required prolonged ICU and overall hospital stay (p=0.001). When compared with GEMS, HEMS group were more likely to sustain severe injuries (ISS>15; p=0.001) and needed frequent on-scene intubation (P=0.001). Hospital mortality was grater in HEMS patients (10% vs 4%, p=0.001) in comparison to GEMS. Multivariate analysis, after adjusting for the confounding factors demonstrated that polytrauma and lower scene GCS were the independent predictors of mortality. CONCLUSIONS: Patients transported by HEMS are characterized by greater injury severity, traumatic brain injury and on-scene intubation. Moreover, the mortality is 2.5 fold greater in patients transported by GEMS, however, the impact of MOT on mortality disappear after adjusting for potential confounders. Institutional guidelines that focus on clinical triage criteria, key environmental factors and reducing transport time may be informed by this study. Further investigations are needed to better quantifyestimate the pre-hospital time intervals and to identify the sub-groups of trauma patients who will clearly benefit from the use of HEMS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".