Factors Influencing Outcomes of Trauma Patients Transferred in Trauma Systems by Air or Ground Ambulance: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: This systematic review aims to determine the effectiveness of ambulance transportation versus helicopter transportation on mortality for trauma patients. METHODS: A systematic review of published and unpublished databases (to August 2023) was performed. Studies, reporting mortality, for people who experienced trauma and were transported to a trauma unit by ambulance or helicopter were eligible. The Newcastle-Ottawa scale was employed to evaluate study quality. RESULTS: Of the 7,323 studies screened, 63 met the inclusion criteria. Thirty-two percent of these studies included patients with diverse injury types, while nine studies included patients across all age groups. The majority (92%) of the included data were retrospective in nature. Eighteen studies (28.57%) achieved the highest score on the Newcastle-Ottawa scale suggesting high-quality evidence. Seven studies examining 24-h mortality reported variable findings. Eighteen studies reported mortality without exact time points through adjusted analyses, 17 favored air transport. Air transport showed an advantage across all subgroups in the adjusted data, while the unadjusted data presented relatively similar outcomes between the two modes of transport. CONCLUSIONS: This systematic review found that adjusted analyses consistently favored air transport over ground transport. Unadjusted analyses showed no significant difference between the two modes of transport, except in specific subgroups. Further subgroup analyses revealed notable disparities between the two modalities, suggesting that these differences may be influenced by multiple factors. These findings highlight the need for further research to clarify the true impact of transport modality on trauma outcomes.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".