The Application of Low-fidelity Chest Tube Insertion Using Remote Telesimulation in Training Healthcare Professionals
Bibliographic record
Abstract
Healthcare professionals practicing in rural, remote, or resource-restricted areas have little opportunity to practice "high stakes low-frequency" clinical procedures, despite having higher rates of injury-related death than city inhabitants. Availability of clinical skills instructors, the expense of practicing skills, lack of educational sessions, and distance to simulation centres can be a barrier to teaching and skill maintenance, particularly in rural settings. Telesimulation has the potential to overcome these challenges using audio-visual technology to connect rural learners with instructors in simulation centres. Using low-fidelity simulation models allows learners to acquire clinical skills through hands-on practice without risk or fear of harming real patients. Although not as realistic as high-fidelity models, the low-fidelity three-dimensional (3D) printed model for chest tube insertion is cost-effective and easy to set up and use and is a valid tool for teaching the clinical procedure. The purpose of this technical report was to describe the application of low-cost telesimulation to facilitate teaching chest tube insertion to medical students, emergency medicine residents, and doctors working in remote and rural environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".