Development and Assessment of a Distal Radial Fracture Model as a Clinical Teaching Tool
Bibliographic record
Abstract
BACKGROUND: Simulation-based learning is increasingly prevalent in the curricula of many surgical training programs. Newly developed simulators must undergo rigorous validity testing before they are used to assess and evaluate surgical trainees. We describe the development of a model that simulates a distal radial fracture requiring closed reduction and cast application and demonstrate its validity. METHODS: We developed a model for simulated treatment of a distal radial fracture with use of a modified Sawbones forearm. Ten junior and ten senior orthopaedic residents were videotaped performing a closed reduction and applying a cast on the model. After each procedure, standard anteroposterior and lateral radiographs of the forearm model were obtained. Two blinded orthopaedic surgeons then rated each resident using a task-specific checklist (Objective Structured Assessment of Technical Skills [OSATS]) and a global rating scale (GRS) as well as radiographic measurements of palmar tilt and three-point index. RESULTS: Compared with the junior residents, senior residents had significantly higher OSATS (p < 0.001) and GRS scores (p < 0.001). The groups did not differ significantly with respect to radiographic palmar tilt (p = 0.86) and three-point index (p = 0.43). All residents were able to restore anatomical alignment, with a mean palmar tilt of 9.1°. In addition, the mean three-point index of all residents was acceptable (0.76). There was a strong correlation between OSATS and GRS scores (r > 0.87; p < 0.01). The inter-rater reliability was high (≥ 0.79) for the OSATS, GRS, and radiographic measurements. CONCLUSIONS: We developed an educational model that simulates a distal radial fracture requiring closed reduction and cast application. We demonstrated construct validity, as the GRS and OSATS tools were able to differentiate senior from junior residents. We were unable to differentiate trainees using radiographic assessment, as all residents restored anatomical alignment and had comparable three-point index scores.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".