Evaluating the Effect of a Cadaver‐Based Video Resource on the Pelvic Binding Competencies of Firefighters
Bibliographic record
Abstract
A pelvic fracture is a life‐threatening injury that requires accurate pre‐hospital care. Pelvic binding is an effective, non‐invasive procedure that can manage haemorrhages associated with most pelvic fractures. However, pelvic binding is a high precision skill that requires proper training to ensure proficiency. Previous studies have shown that pelvic binding is both irregularly and inaccurately performed at several tiers of emergency medicine. One plausible explanation for this competency issue is that training associated with pelvic binding is often brief and does not clearly explain the ‘why’ behind the procedure. Using a compilation of cadaveric images that emphasized the important anatomy related to pelvic binding, a cadaver‐based video resource was created to supplement traditional teaching of this skill. The present study examined the effect of this cadaver‐based video resource on the pelvic binding competencies of emergency responders, specifically firefighters. The study used a double‐blinded approach – participants (n = 16) were sorted into two groups that were balanced according to their previous first‐aid experience. The control group was given 20 minutes to practice the skill of pelvic binding, while the intervention group was given a three‐minute cadaver‐based video resource that focused on the anatomy of the pelvis and pelvic binding, followed by 17 minutes to practice the skill of pelvic binding. Over three visits, participants performed three written and three clinical competency tests to assess their pelvic binding knowledge and their ability to apply a pelvic binder. The first set of tests were administered one week prior to the intervention to record baseline competency. The second set of tests were administered post‐intervention to measure any changes in scores. The final set of tests were administered three weeks after the intervention to assess knowledge retention. The primary outcome measures evaluated criteria related to the proper placement of the pelvic binder. The written tests assessed whether the participant knew the location at which the pelvic binder should be applied, while the clinical competency tests assessed whether the participant could translate that knowledge and successfully apply the pelvic binder to a subject. Preliminary results showed a positive trend in both knowledge of pelvic binding and performance of the skill favouring the group who had access to the video resource. According to Fisher's Exact test, a significant difference in scores associated with pelvic binding accuracy was observed (p=0.026) between the control and intervention groups. The intervention group identified the correct landmark for binder placement with 100% accuracy, while the control group correctly identified the landmark only 37.5% of the time. These findings suggest that targeted cadaver‐based training videos may be valuable tools for the development of clinical competencies. Overall, this study may offer insights into the development of cadaver‐based educational resources to supplement training protocols and enhance the understanding of emergency responders. By explaining the anatomy involved in emergency procedures, the ‘why’ behind clinical protocols can be clarified. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".