Smarter faster just-in-time hemorrhage control: A pilot evaluation of remotely piloted aircraft system delivered STOP-THE-BLEED equipment with just-in-time remote telementored deployment
Bibliographic record
Abstract
Introduction: Remotely Piloted Aircraft Systems (RPAS) can access patients inaccessible to traditional rescue. Just-in-time remote telementoring (RTM) of naïve users to self-care could potentially address challenges in salvaging exsanguination in remote environments. Methods: An exsanguination self-application task was established in a wilderness location. Three volunteers-initiated distress calls to prompt RPAS precision delivered STOP-THE-BLEED kits, after which a remote mentor directed the volunteers how to self-care. Results: Limited connectivity prevented video, however each volunteer delivered images and initiated conversation with the mentor pre-RPAS arrival. Thereafter, all subjects were able to unpack and deploy hemorrhage control adjuncts under verbal direction, and to simulate self-application. All subjects were able to successfully apply wound-clamps, tourniquets, and pack wounds although one had insufficient pressure. Discussion: RPASs can deliver supplies long before human rescuers, and communication connectivity might allow remote mentoring in device application. Further development of technology and self-care paradigms for exsanguination are encouraged.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".