Assessment of competence in antegrade intramedullary nail osteosynthesis of femoral shaft fractures: A global Delphi consensus study
Bibliographic record
Abstract
Introduction: Antegrade intramedullary nailing of femoral shaft fractures (FSF) is a core competency in orthopedic surgery. However, trainees' skills acquisition is hindered due to reduced exposure to the procedure. Competency-based medical education (CBME) and simulation-based training (SBT) offer an alternative to traditional time-based residency models; however, their implementation in FSF fixation requires assessment tools supported by validity evidence. This study aimed to use the Delphi method to establish consensus regarding assessment parameters for antegrade FSF nailing. Materials and methods: A modified Delphi study was conducted with a global panel of AO trauma faculty educators. In round 1, panelists proposed key technical skills and common errors during FSF fixation. Round 2 involved rating parameter importance on a 5-point Likert-like scale. In Round 3, specific score ranges were determined for a specific fracture model; these results are not presented in this study. In the final round, each parameter was assigned a weight from 1 to 10. Pearson's correlation coefficient was calculated between round 2 and the final round. Results: Of 98 panelists included, 87 actively participated. Round 1 yielded 37 parameters. Consensus was reached for 34 after round 2. The mean importance rating in round 2 was 4.04 (SD 0.34), and the mean weight rating in the final round was 8.56 (SD 0.62). A strong correlation was found between importance and weight ratings (r = 0.94, p < 0.001). The final 34 parameters cover the entire fixation process, with 2 relating to fracture reduction, 6 to guidewire placement and entry point, 4 to reaming, 3 to nail choice, 6 to nail placement, 9 to interlocking, and 4 to the end of the procedure. Discussion: This study defined 34 expert-derived parameters for intramedullary FSF fixation. They are well-suited for implementation in CBME programs and simulators, ensuring content validity and supporting structured skills training.
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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 | 0.083 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".