Clinical assessment to identify pelvic injuries in the prehospital setting: a prospective cohort study
Bibliographic record
Abstract
Objectives Pelvic injuries can be life-threatening and are challenging to identify in the prehospital phase. This study aimed to assess how pelvic examination is performed by paramedics and to determine the accuracy of their clinical examination when identifying pelvic fractures. Methods This was a prospective cohort study of prehospital interventions including both real and simulated trauma patients between July and August 2022. Data collection for the injured patient was made by an observer who was paired with teams of two consenting paramedics. Data pertaining to the clinical assessment for potential pelvic injuries during all interventions with a trauma patient were collected. Additionally, data were collected during four simulated scenarios of patients with an open-book type pelvic fracture following high-energy trauma mechanisms. Results A total of 29 trauma-related clinical interventions were assessed. Most patients were female ( n = 22, 75.9%) with a mean age of 69.8 (SD 22.2) years. Fall from standing was the main trauma mechanism ( n = 21, 72.4%). During 72.4% ( n = 21) of all trauma-related interventions, an assessment for pelvic injuries was performed, mostly by pelvic palpation ( n = 19, 65.5%), inquiring about the presence of pain ( n = 12, 41.4%), searching for deformation ( n = 7, 24.1%), and/or assessing leg length ( n = 8, 27.6%). No pelvic injury was suspected by the paramedics, but two patients (6.9%) had a pelvic fracture and two (6.9%) had a hip fracture. Simulated cases of high-velocity mechanisms with an open-book pelvic fracture were completed by 11 pairs of paramedics. Most did a clinical pelvic assessment ( n = 8, 72.7%) using palpation. When asked after the simulation, nine pairs (81.8%) suspected a pelvic fracture. Conclusion Pelvic injuries are challenging to identify, and pelvic assessment on the field is not standardized among paramedics. Training is needed to increase awareness relative to pelvic injuries and to improve detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".