The marriage of surgical simulation and telementoring for damage-control surgical training of operational first responders
Bibliographic record
Abstract
BACKGROUND: Hemorrhage is the leading cause of preventable posttraumatic death. Many such deaths may be potentially salvageable with remote damage-control surgical interventions. As recent innovations in information technology enable remote specialist support to point-of-care providers, advanced interventions, such as remote damage-control surgery, may be possible in remote settings. METHODS: An anatomically realistic perfused surgical training mannequin with intrinsic fluid loss measurements (the "Cut Suit") was used to study perihepatic packing with massive liver hemorrhage. The primary outcome was loss of simulated blood (water) during six stages, namely, incision, retraction, direction, identification, packing, and postpacking. Six fully credentialed surgeons performed the same task as 12 military medical technicians who were randomized to remotely telementored (RTM) (n = 7) or unmentored (UTM) (n=5) real-time guidance by a trauma surgeon. RESULTS: There were no significant differences in fluid loss between the surgeons and the UTM group or between the UTM and RTM groups. However, when comparing the RTM group with the surgeons, there was significantly more total fluid loss (p = 0.001) and greater loss during the identification (p = 0.002), retraction (p = 0.035), direction (p = 0.014), and packing(p = 0.022) stages. There were no significant differences in fluid loss after packing between the groups despite differences in the number of sponges used; RTM group used more sponges than the surgeons and significantly more than the UTM group (p = 0.048). However, mentoring significantly increased self-assessed nonsurgeon procedural confidence (p = 0.004). CONCLUSION: Perihepatic packing of an exsanguinating liver hemorrhage model was readily performed by military medical technicians after a focused briefing. While real-time telementoring did not improve fluid loss, it significantly increased nonsurgeon procedural confidence, which may augment the feasibility of the concept by allowing them to undertake psychologically daunting procedures.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".