The impact of shorter prehospital transport times on outcomes in patients with abdominal vascular injuries
Bibliographic record
Abstract
BACKGROUND: Most deaths in patients with abdominal vascular injuries (ABVI) are caused by exsanguination and irreversible shock. Therefore, time to definitive hemorrhage control is an important factor affecting survival. The study goals were: (1) document current outcomes in patients with ABVI, and (2) compare outcomes to those from the era preceding improvements in an urban prehospital system. METHODS: A retrospective review of all patients with ABVI at an urban level 1 trauma center was completed. Patients injured prior to prehospital transport improvements (1991-1994) were compared to those following a reduction in transport times (1995-2004). RESULTS: Of 388 patients, 70 (18%) arrived prior to prehospital improvements (1991-1994). Patient/injury demographics were similar in both groups (age, sex, penetrating mechanism; p > 0.05). The number of patients presenting with ABVI increased (23 vs. 35 per year; p < 0.05) concurrent to a reduction in transport times (27 vs. 20 minutes; p < 0.05). Patients were more frequently unstable (63% vs. 91%; p < 0.05). Regardless of the specific vessel, mortality increased (37% vs. 67%; p < 0.05) following prehospital improvements. CONCLUSIONS: A reduction in urban transport times resulted in an increase in (1) the number of patients arriving with abdominal vascular injuries, (2) the proportion presenting in physiologic extremis, and (3) overall mortality.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".