Development of an assessment tool for open reduction and internal fixation of midshaft ulnar fractures: A global delphi consensus study
Bibliographic record
Abstract
OBJECTIVES: In acknowledgement of the ongoing transition of surgical education from a time-based approach to competency-based curricula, this study aimed to identify key parameters for assessing the performance of surgical trainees in open reduction and internal fixation (ORIF) of a simple ulnar shaft fracture (AO/OTA classification 2U2A3.B). METHODS: A 4-round Delphi process regarding seven different orthopedic osteosynthesis surgeries was conducted with an international panel of orthopedic surgeons involved in surgical education. This manuscript focuses on compression plating of isolated ulna fractures. Round 1 focused on item generation, round 2 on importance rating, round 3 on defining optimal intervals and borderline error values for a specific fracture model (not reported in this manuscript), and round 4 on assigning weights to each parameter. Data collection was carried out online. RESULTS: Ninety-eight surgeons agreed to participate in the study. Round 1 generated 30 assessment parameters. In round 2 and 3, these were reduced to 26 parameters. In round 4, parameters received an overall mean weight of 8.27 out of 10 (SD 0.66) with a range of individual parameter mean weights from 6.7 to 9.4. The assessment parameters that achieved the highest weights were anatomical fracture reduction and assessment of forearm range of motion after fixation. In the final list of parameters, five were related to fracture reduction, three to hardware choice, five to plate placement, nine to screw placement, and four to concluding the procedure. CONCLUSIONS: Utilizing a Delphi process, expert consensus was reached generating a comprehensive list of 26 assessment parameters that can be used to assess surgeon performance in open reduction and internal fixation of an isolated adult ulnar shaft fracture. This will allow educators to provide standardized feedback (formative assessment) to trainees and use a mastery-learning training approach (summative assessment).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".