Technical innovations that may facilitate real-time telementoring of damage control surgery in austere environments: a proof of concept comparative evaluation of the importance of surgical experience, telepresence, gravity and mentoring in the conduct of damage control laparotomies
Bibliographic record
Abstract
Bleeding to death is the most preventable cause of posttraumatic death worldwide. Despite the fact that many of these deaths are anatomically salvageable with relatively basic surgical interventions, they remain lethal in actuality in prehospital environments when no facilities and skills exist to contemplate undertaking basic damage control surgery (DCS). With better attention to prehospital control of extremity hemorrhage, intracavitary bleeding (especially intraperitoneal) remains beyond the scope of prehospital providers. However, recent revolutions in the informatics and techniques of telementoring (TMT), DCS and highly realistic accelerated training of motivated first responders suggests that basic lifesaving DCS may have applicability to save bleeding patients in austere environments previously considered unsalvageable. Especially with informatic advances, any provider with Internet connectivity can potentially be supported by highly proficient specialists with content expertise in the index problem. This unprecedented TMT support may allow highly motivated but inexperienced personnel to provide advanced surgical interventions in extreme environments in many austere locations both on and above the planet.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".