To Watch Before or Listen While Doing? A Randomized Pilot of Video-Modelling versus Telementored Tube Thoracostomy
Bibliographic record
Abstract
BACKGROUND: New care paradigms are required to enable remote life-saving interventions (RLSIs) in extreme environments such as disaster settings. Informatics may assist through just-in-time expert remote-telementoring (RTM) or video-modelling (VM). Currently, RTM relies on real-time communication that may not be reliable in some locations, especially if communications fail. Neither technique has been extensively developed however, and both may be required to be performed by inexperienced providers to save lives. A pilot comparison was thus conducted. METHODS: Procedure-naïve Search-and-Rescue Technicians (SAR-Techs) performed a tube-thoracostomy (TT) on a surgical simulator, randomly allocated to RTM or VM. The VM group watched a pre-prepared video illustrating TT immediately prior, while the RTM group were remotely guided by an expert in real-time. Standard outcomes included success, safety, and tube-security for the TT procedure. RESULTS: There were no differences in experience between the groups. Of the 13 SAR-Techs randomized to VM, 12/13 (92%) placed the TT successfully, safely, and secured it properly, while 100% (11/11) of the TT placed by the RTM group were successful, safe, and secure. Statistically, there was no difference (P = 1.000) between RTM or VM in safety, success, or tube security. However, with VM, one subject cut himself, one did not puncture the pleura, and one had barely adequate placement. There were no such issues in the mentored group. Total time was significantly faster using RTM (P = .02). However, if time-to-watch was discounted, VM was quicker (P = .000). CONCLUSIONS: Random evaluation revealed both paradigms have attributes. If VM can be utilized during "travel-time," it is quicker but without facilitating "trouble shooting." On the other hand, RTM had no errors in TT placement and facilitated guidance and remediation by the mentor, presumably avoiding failure, increasing safety, and potentially providing psychological support. Ultimately, both techniques appear to have merit and may be complementary, justifying continued research into the human-factors of performing RLSIs in extreme environments that are likely needed in natural and man-made disasters.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".