<i>Ex vivo</i>Contact Mechanics and Three‐Dimensional Alignment of Normal Dog Elbows after Proximal Ulnar Rotational Osteotomy
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of proximal ulnar rotational osteotomy (PURO) on the contact mechanics and 3-dimensional (3D) alignment of cadaveric normal dog elbows. STUDY DESIGN: Ex vivo biomechanical study. ANIMALS: Unpaired thoracic limbs from 12 dogs (mean ± SD weight, 26 ± 4 kg). METHODS: PURO consisted of a transverse osteotomy with 30° external rotation of the proximal segment. With the limb under 200 N axial load, contact area (CA), mean contact pressure (CP) and peak contact pressure (PCP) were measured using digital pressure sensors in the medial and lateral compartments and 3D static elbow poses were obtained, before and after PURO. Each specimen was tested at 115°, 135°, and 155° elbow flexion, with the antebrachium in neutral rotation, in 28° supination, and in 16° pronation. Repeated measures ANOVAs with post-hoc Bonferroni (P ≤ .05) were performed. RESULTS: PURO caused significant changes, mostly at 135° elbow flexion and neutral antebrachial rotation, characterized by decreased CP (10%) and PCP (10%) in the medial compartment, and increased CP (27%) and PCP (23%) in the lateral compartment. The apex of the medial coronoid process translated 3.4 mm caudally, 1.7 mm abaxially, and rotated 1.8° externally relative to the radial head. Humeroradial varus angulation reduced by 6.7°. CONCLUSIONS: In limited poses, PURO displaces the medial coronoid process caudally and abaxially and shifts contact pressures towards the lateral elbow compartment by decreasing varus angulation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".