A Randomized Trial of Mentored vs Nonmentored Military Medics Compared in the Application of a Wound Clamp Without Prior Training: When to Shut Up and Just Watch!
Bibliographic record
Abstract
INTRODUCTION: Hemorrhage control is a basic task required of first responders and typically requires technical interventions during stressful circumstances. Remote telementoring (RTM) utilizes information technology to guide inexperienced providers, but when this is useful remains undefined. METHODS: Military medics were randomized to mentoring or not from an experienced subject matter expert during the application of a wound clamp (WC) to a simulated bleed. Inexperienced, nonmentored medics were given a 30-second safety briefing; mentored medics were not. Objective outcomes were time to task completion and success in arresting simulated bleeding. RESULTS: Thirty-three medics participated (16 mentored and 17 nonmentored). All (100%) successfully applies the WC to arrest the simulated hemorrhage. RTM significantly slowed hemorrhage control (P = 0.000) between the mentored (40.4 ± 12.0 seconds) and nonmentored (15.2 ± 10.3 seconds) groups. On posttask questionnaire, all medics subjectively rated the difficulty of the wound clamping as 1.7/10 (10 being extremely hard). Discussion: WC application appeared to be an easily acquired technique that was effective in controlling simulated extremity exsanguination, such that RTM while feasible did not improve outcomes. Limitations were the lack of true stress and using simulation for the task. Future research should focus on determining when RTM is useful and when it is not required.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".