Air versus ground transport of major trauma patients to a tertiary trauma centre: a province-wide comparison using TRISS analysis.
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to compare the outcomes of adult (aged > 15 yr) blunt trauma patients with an Injury Severity Score (ISS) = 12 who were transported to a single tertiary trauma centre (TTC) by helicopter emergency medical service (HEMS) versus those transported by ground ambulance. METHODS: We retrospectively analyzed all adult (aged > 15 yr) trauma patients between March 27, 1998 and March 28, 2002 with an ISS score = 12, as identified through the provincial trauma registry. We used the Trauma and Injury Severity Score (TRISS) methodology to determine a difference in outcomes between the 2 groups. RESULTS: We identified 823 patients; of these, we excluded 32 (3.9%) penetrating trauma patients. Of the blunt trauma cases (n = 791) 237 (30%) patients were transported by air and 554 were transported by ground (70%). A total of 770 (97.3%) patients were eligible for TRISS analysis. Using the TRISS methodology, the air group had a Z statistic of 2.77, yielding a W score of 6.40. This compared with the ground transport group, whose Z statistic was 1.97 and W score was 2.39. CONCLUSION: The transport of trauma patients with an ISS = 12 by a provincially dedicated rotor wing air medical service was associated with statistically significantly better outcomes than those transported by standard ground ambulance. This is the first large Canadian study to specifically compare the outcome of patients transported by ground with those transported by air.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".