Robotically applied hemostatic clamping for care-under-fire: harnessing bomb robots for hemorrhage control
Bibliographic record
Abstract
Background: Early hemorrhage control after interpersonal violence is the most urgent requirement to preserve life and is now recognized as a responsibility of law enforcement. Although earlier entry of first responders is advocated, many shooting scenes remain unsafe for humans, necessitating first responses conducted by robots. Thus, robotic hemorrhage control warrants study as a care-under-fire treatment option. Methods: Two bomb disposal robots (Wolverine and Dragon Runner) were retrofitted with hemostatic wound clamps. The robots’ ability to apply a wound clamp to a simulated extremity exsanguination while controlled by 4 experienced operators was tested. The operators were randomly assigned to perform 10 trials using 1 robot each. A third surveillance robot (Stair Climber) provided further visualization for the operators. We assessed the success rate of the application of the wound clamp to the simulated wound, the time to application of the wound clamp and the amount of fluid loss. We also assessed the operators’ efforts to apply the wound clamp after an initial attempt was unsuccessful or after the wound clamp was dropped. Results: Remote robotic application of a wound clamp was demonstrated to be feasible, with complete cessation of simulated bleeding in 60% of applications. This finding was consistent across all operators and both robots. There was no difference in the success rates with the 2 robots (p = 1.00). However, there were differences in fluid loss (p = 0.004) and application time (p < 0.001), with the larger (Wolverine) robot being faster and losing less fluid. Conclusion: Law enforcement tactical robots were consistently able to provide partial to complete hemorrhage control in a simulated extremity exsanguination. Consideration should be given to using this approach in care-under-fire and care-behind-the-barricade scenarios as well as further developing the technology and doctrine for robotic hemorrhage control.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".