Objective Analysis of Lateral Elbow Exposure with the Extensor Digitorum Communis Split Compared with the Kocher Interval
Bibliographic record
Abstract
BACKGROUND: The most widely used surgical approach to treat radial head fractures is through the Kocher interval. However, the extensor digitorum communis (EDC) splitting approach is thought to allow easier access to the anterior half of the radial head, which is more commonly fractured. The aim of this cadaveric study was to compare the osseous and articular surface areas visible through the EDC split and the Kocher interval. METHODS: Four approaches were used in fresh frozen cadaveric upper extremities: EDC splitting (n = 6), modified Kocher (n = 6), extended EDC splitting (n = 6), and extended modified Kocher (n = 4). For each approach, the osseous and articular surface areas visualized were outlined with use of a burr. Each elbow was then stripped of soft tissue and a digitized three-dimensional model was created with use of a surface scanning system. The visible surface area obtained with each approach was mapped and quantified with use of the markings created with the burr. RESULTS: The EDC splitting approach provided greater exposure of the anterior half of the radial head (median, 100%) compared with the modified Kocher approach (68%, p < 0.05). The extended modified Kocher and extended EDC splitting approaches provided comparable visualization of the distal aspect of the humerus, capitellum, radial head, and coronoid process. CONCLUSIONS: The results suggest that the EDC splitting approach provides more reliable visualization of the anterior half of the radial head while minimizing soft-tissue dissection and reducing the risk of iatrogenic injury to the lateral ulnar collateral ligament.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".