A Randomized Comparison of Clothing Removal Techniques in a Simulated Trauma Patient Exposure
Bibliographic record
Abstract
Introduction Trauma shears are commonly used by emergency medical services (EMS) providers to remove clothing from patients and expose underlying traumatic injuries. Other tools exist that may be more effective, but they are largely untested. This study compared the use of trauma shears versus two cutting hooks in removing clothing from a simulated trauma patient. Methods We recruited 18 paramedic students to participate in a cross-over study designed to remove clothing from a wholly dressed full-body training mannequin using trauma shears (with the cut-and-rip (CAR) technique) and two cutting hooks (S-Cut QE (ES Equipment AB, Nol, Sweden) and the Talon Rescue Emergency Clothing Knife (TRECK+, Talon Rescue, Farmington, CT, USA)). We determined the order of the tools using a three-by-three Latin square and randomized participants equally between possible orders to minimize carryover effects. We recorded times for total clothing removal and the removal of clothing from the upper and lower body, respectively. We employed a mixed-effects analysis of variance (ANOVA) to determine any differences between tools. Results Removal time was significantly faster with the S-Cut QE compared to the CAR technique and TRECK+ (mean 78 seconds, 95% confidence interval (CI) 52-103 vs. 142 seconds, 95% CI 117-167, vs. 209 seconds, 95% CI 184-235, p<0.001). The S-Cut QE was significantly faster than the CAR technique and TRECK+ for upper body clothing removal (mean 47 seconds, 95% CI 30-64 vs. 92 seconds, 95% CI 75-109, vs. 131 seconds, 95% CI 115-148, p<0.001) and the S-Cut QE and CAR were significantly faster than TRECK+ for lower body clothing removal (mean 25 seconds, 95% CI 11-38 and 44 seconds, 95% CI 31-58 vs. 71 seconds, 95% CI 58-85, p<0.001). Most (78%) participants preferred the S-Cut QE over other tools. Conclusion The S-Cut QE removed clothing from a simulated trauma patient faster than both the CAR and TRECK+. Emergency medical services (EMS) agencies should consider adding a cutting hook to their standard trauma kit.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".