Variability and repeatability of the flexion axis at the ulnohumeral joint
Bibliographic record
Abstract
Previous investigations have implemented screw displacement axes (SDAs) to define the elbow flexion axis for proper positioning of dynamic external fixators and endoprostheses. However, results across studies vary, which may be attributed to forearm position (pronation-supination) during elbow motion, or the mode of loading (active/passive) employed to generate flexion. Therefore, the aim of this study was to determine the influence of the flexion mode employed and forearm position on individual variation and repeatability of SDAs throughout elbow flexion. With the forearm pronated, the location of the average SDA was similar whether elbow flexion was generated actively or passively. In contrast, with the forearm supinated, the average SDA was 2.4 degrees and 1.4 degrees more valgus (p<0.001) and internally rotated (p<0.001), respectively, and positioned 1.6 and 0.8 mm further proximally (p=0.002) and anteriorly (p=0.005) relative to the capitellum, respectively, during active compared to passive flexion. During active flexion, the location of the average SDA was independent of forearm position. Conversely, during passive flexion, the average SDA angle was 3.4 degrees and 1.0 degrees more valgus (p<0.001) and internally rotated (p=0.009), respectively, and 1.7 and 0.7 mm more proximal (p<0.001) and anterior (p=0.001) relative to the capitellum, respectively, with the forearm held pronated rather than supinated. SDAs calculated throughout flexion deviated from the average SDA in both orientation and position, demonstrating that elbow flexion behaves similar to a loose hinge joint. These factors suggest that to encompass the location of all SDAs throughout flexion, and therefore properly mimic normal elbow joint motion, an endoprosthesis should be modeled similar to a "loose" rather than "pure" hinge joint. This would allow for dependencies of SDA angulation on forearm position and muscle activation, and slight freedom of movement to account for variances in SDA location. These factors should also be considered during soft-tissue reconstructions.
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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 | 0.011 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".