Skill Acquisition and Retention Following Simulation-Based Training in Pavlik Harness Application
Bibliographic record
Abstract
BACKGROUND: Simulation-based learning is increasingly prevalent in many surgical training programs, as medical education moves toward competency-based curricula. In orthopaedic surgery, developmental dysplasia of the hip is a commonly treated condition, where the standard of care for patients less than six months of age is an orthotic device such as the Pavlik harness. However, despite widespread use of the Pavlik harness and the potential complications that may arise from inappropriate application, we know of no previously described formal training curriculum for Pavlik harness application. METHODS: We developed a video and model-based simulation learning module for Pavlik harness application. Two novice groups (residents and allied health professionals) were exposed to the module and, at pre-intervention, post-intervention, and retention testing, were evaluated on their ability to apply a Pavlik harness to the model. Evaluations were completed using a previously validated Objective Structured Assessment of Technical Skills (OSATS) and a global rating scale (GRS) specific to Pavlik harness application. A control group that did not undergo the module was also evaluated at two time points to determine if exposure to the Pavlik harness alone would affect skill acquisition. All groups were compared with a group of clinical experts, whose scores were used as a competency benchmark. Statistical analysis of skill acquisition and retention was conducted using t tests and analysis of variance (ANOVA). RESULTS: Exposure to the learning module improved resident and allied health professionals' competency in applying a Pavlik harness (p < 0.05) to the level of the expert clinicians, and this level of competency was retained one month after exposure to the module. Control subjects who were not exposed to the module did not improve, nor did they achieve competency. CONCLUSIONS: The simulation-based learning module was shown to be an effective tool for teaching the application of a Pavlik harness, and learners demonstrated retainable skills post-intervention. This learning module can form the cornerstone of formal teaching of Pavlik harness application for developmental dysplasia of the hip.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".