Augmented-reality telementoring for leg fasciotomy: a proof-of-concept study
Bibliographic record
Abstract
BACKGROUND: Prolonged field care is required when casualty evacuation to a surgical facility is delayed by distance, weather or tactical constraints. This situation can occur in both civilian and military environments. In these circumstances, there are no established treatment options for extremity compartment syndrome. Telementoring by a surgeon may enable the local clinician to perform a fasciotomy to decompress the affected compartments. METHODS: Six military clinicians were asked to perform a two-incision leg fasciotomy in synthetic models under the guidance of an orthopaedic surgeon located 380 km away. Communication occurred through commercially available software and smartglasses, which also allowed the surgeon to send augmented-reality graphics to the operators. Two blinded surgeons evaluated the specimens according to objective criteria. Control specimens were added to ensure the integrity of the evaluation process. RESULTS: The six study participants were military physician assistants who had extensive clinical experience but had never performed a fasciotomy. The average duration of the procedure was 53 min. All six procedures were completed without major errors: release of all four compartments was achieved through full-length incisions in the skin and fascia. The only surgical complication was a laceration of the saphenous vein. All three control specimens were correctly assessed by the evaluators. None of the participants experienced adverse effects from wearing the smartglasses. Four dropped calls occurred, but the connection was re-established in all cases. CONCLUSION: All six surgical procedures were completed successfully. We attribute the dropped calls to a mismatch between the size of the graphic files and the available bandwidth. A better technical understanding of the software by the mentoring surgeon would have avoided this problem. Important considerations for future research and practice include protocols for dropped communications, surgical skills training for the operators and communication training for the surgeons.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".