The Damage Control Surgery in Austere Environments Research Group (DCSAERG)
Bibliographic record
Abstract
Hemorrhage is the most preventable cause of posttraumatic death. Many cases are potentially anatomically salvageable, yet remain lethal without logistics or trained personnel to deliver diagnosis or resuscitative surgery in austere environments. Revolutions in technology for remote mentoring of ultrasound and surgery may enhance capabilities to utilize the skill sets of non-physicians. Thus, our research collaborative explored remote mentoring to empower non-physicians to address junctional and torso hemorrhage control in austere environments. Major studies involved using remote-telementored ultrasound (RTMUS) to identify torso and junctional exsanguination, remotely mentoring resuscitative surgery for torso hemorrhage control, understanding and mitigating physiological stress during such tasks, and the technical practicalities of conducting damage control surgery (DCS) in austere environments. Iterative projects involved randomized guiding of firefighters to identify torso (RCT) and junctional (pilot) hemorrhage using RTMUS, randomized remote mentoring of MedTechs conducting resuscitative surgery for torso exsanguination in an anatomically realistic surgical trainer ("Cut Suit") including physiological monitoring, and trained surgeons conducting a comparative randomized study for torso hemorrhage control in normal (1g) versus weightlessness (0g). This work demonstrated that firefighters could be remotely mentored to perform just-in-time torso RTMUS on a simulator. Both firefighters and mentors were confident in their abilities, the ultrasounds being 97% accurate. An ultrasound-naive firefighter in Memphis could also be remotely mentored from Hawaii to identify and subsequently tamponade an arterial junctional hemorrhage using RTMUS in a live tissue model. Thereafter, both mentored and unmentored MedTechs and trained surgeons completed resuscitative surgery for hemorrhage control on the Cut-Suit, demonstrating practicality for all involved. While remote mentoring did not decrease blood loss among MedTechs, it increased procedural confidence and decreased physiologic stress. Therefore, remote mentoring may increase the feasibility of non-physicians conducting a psychologically daunting task. Finally, DCS in weightlessness was feasible without fundamental differences from 1g. Overall, the collective evidence suggests that remote mentoring supports diagnosis, noninvasive therapy, and ultimately resuscitative surgery to potentially rescue those exsanguinating in austere environments and should be more rigorously studied.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".