Rubrum Coelis: The Contribution of Real-Time Telementoring in Acute Trauma Scenarios—A Randomized Controlled Trial
Bibliographic record
Abstract
Background: Most deaths in military trauma occur soon after wounding, and demand immediate on scene interventions. Although hemorrhage predominates as the cause of potentially preventable death, airway obstruction and tension pneumothorax are also frequent. First responders caring for casualties in operational settings often have limited clinical experience. Introduction: We hypothesized that communications technologies allowing for real-time communications with a senior medically experienced provider might assist in the efficacy of first responding to catastrophic trauma. Methods: Thirty-three basic life saving (BLS) medics were randomized into two groups: either receiving telementoring support (TMS, n = 17) or no telementoring support (NTMS, n = 16) during the diagnosis and resuscitation of a simulated critical battlefield casualty. In addition to basic life support, all medics were required to perform a procedure needle thoracentesis (not performed by BLS medics in Israel) for the first time. TMS was performed by physicians through an internet link. Performance was assessed during the simulation and later on review of videos. Results: The TMS group was significantly more successful in diagnosing (82.35% vs. 56.25%, p = 0.003) and treating pneumothorax (52.94% vs. 37.5%, p = 0.035). However, needle thoracentesis time was slightly longer for the TMS group versus the NTMS group (1:24 ± 1:00 vs. 0:49 ± 0:21 minu, respectively (p = 0.016). Complete treatment time was 12:56 ± 2:58 min for the TMS group, versus 9:33 ± 3:17 min for the NTMS group (p = 0.003). Conclusions: Remote telementoring of basic life support performed by military medics significantly improved the medics' ability to perform an unfamiliar lifesaving procedure at the cost of prolonging time needed to provide care. Future studies must refine the indications and contraindications for using telemedical support.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".