Identification and Management of Pelvic Fractures in Prehospital and Emergency Department Settings
Bibliographic record
Abstract
INTRODUCTION: This study aims to describe the characteristics of patients with a pelvic fracture treated at a level 1 trauma center, the proportion of prehospital undertriage and the use of pelvic circumferential compression device (PCCD). METHODS: This is a retrospective cohort study. Prehospital and inhospital medical records of adults (≥16 y old) with a pelvic fracture who were treated at Hopital de l'Enfant-Jesus-CHU de Québec (Quebec City, Canada), a university-affiliated level 1 trauma center, between September 01, 2017 and September 01, 2021 were reviewed. Isolated hip or pubic ramus fracture were excluded. Data are presented using proportions and means with standard deviations. RESULTS: A total of 228 patients were included (males: 62.3%; mean age: 54.6 [standard deviation 21.1]). Motor vehicle collision (47.4%) was the main mechanism of injury followed by high-level fall (21.5%). Approximately a third (34.2%) needed at least one blood transfusion. Compared to those admitted directly, transferred patients were more likely to be male (73.0% versus 51.3%, P < 0.001) and to have a surgical procedure performed at the trauma center (71.3% versus 46.9%, P < 0.001). The proportion of prehospital undertriage was 22.6%. Overall, 17.1% had an open-book fracture and would have potentially benefited from a prehospital PCCD. Forty-six transferred patients had a PCCD applied at the referral hospital of which 26.1% needed adjustment. CONCLUSIONS: Pelvic fractures are challenging to identify in the prehospital environment and are associated with a high undertriage of 22.6%. Reducing undertriage and optimizing the use of PCCD are key opportunities to improve care of patients with a pelvic fracture.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".