Evaluating the Utility of the Lateral Elbow Radiograph in Central Articular Olecranon Reduction: An Anatomic and Radiographic Study
Bibliographic record
Abstract
OBJECTIVES: The surgical reduction of intra-articular olecranon fractures is judged primarily on the lateral elbow radiograph, as orthogonal imaging of the articular surface is not obtainable. We sought to determine surgeon accuracy in identifying intra-articular olecranon malreductions on the lateral elbow radiograph. METHODS: Six human fresh-frozen cadaveric elbow specimens were sagittally sectioned in 5-mm increments after olecranon dissection, preservation of soft tissue envelope, and rigid fixation of the elbow in an external fixator. Three patterns of central intra-articular olecranon malreduction were created in each elbow using a ruler and bone saw. Perfect lateral elbow radiographs were taken of each malreduction, and these images were randomized along with x-rays of normal cadaveric olecranons. The image series was presented to 4 masked trauma-trained surgeons to determine whether the olecranon was malreduced or anatomic. Surgeons interpreted the same image series on 2 separate occasions separated by 6 weeks. Percent correct was recorded, and the interobserver and intraobserver reliability was calculated. RESULTS: Orthopedic trauma surgeons correctly identified olecranon malreductions only 73% of the time on the lateral elbow radiograph. Interobserver agreement was moderate for the first review of images and fair for the second review, with respective Fleiss Kappa values of 0.43 and 0.28. Intrarater reliability revealed moderate agreement with Cohen's Kappa value ranging from 0.56 to 0.66. CONCLUSIONS: Intra-articular olecranon malreductions are inconsistently recognized by trauma surgeons on the lateral elbow radiograph. Therefore, articular incongruity may still be present after surgical fixation of comminuted olecranon fractures. We must further define the radiographic anatomic representation of the articular olecranon to improve surgical reduction and clinical outcomes.
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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 | 0.008 | 0.019 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".