Morphological and Apparent‐Level Stiffness Variations Between Normal and Osteoarthritic Bone in the Humeral Head
Bibliographic record
Abstract
ABSTRACT Osteoarthritis (OA) is characterized by morphological changes that alter bone structure and mechanical properties. This study compared bone morphometric parameters and apparent modulus between humeral heads excised from end‐stage OA patients undergoing total shoulder arthroplasty (n = 28) and non‐pathologic normal cadavers (n = 28). Morphometric parameters were determined in central cores, with regional variations compared in four medial to lateral regions. Linear regression compared apparent modulus, morphometric parameters, and age. Micro finite element models estimated trabecular apparent modulus and derived density–modulus relationships. Significant differences were found for bone volume fraction (p < 0.001) and trabecular thickness (p < 0.001) in the most medial regions. No significant differences occurred between morphometric parameters and apparent modulus or age, except in slope between groups for apparent modulus versus trabecular number (p = 0.021), and in intercept for trabecular thickness versus age (p = 0.040). Significant differences occurred in both slope and intercept between density–modulus regression fits for each group (p ≤ 0.001). The normal group showed high correlations in the power‐fit (r2 = 0.87), with a lower correlation (r2 = 0.61) and a more linear relationship, in the OA group. This study suggests that alterations in structure and apparent modulus persist mainly in subchondral regions of end‐stage OA bone. As such, if pathologic regions are removed during joint replacement, computational models that utilize modeling parameters from non‐pathologic normal bone may be applied to end‐stage OA bone. An improved understanding of humeral trabecular bone variations has potential to improve the surgical management of end‐stage OA patients. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:503–509, 2020
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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 | 0.001 | 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) | 0.001 | 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".