Structure modeling of the glenoid: Relevance to shoulder arthroplasty
Bibliographic record
Abstract
We applied shape modeling and principal component analysis (PCA) to discover glenoid bone structural relationships relevant to improving glenoid prosthesis features, fixation, and instrumentation. Knowledge of external bone morphology guides prosthesis shape and positioning, while internal bone morphology and bone density influence fixation. CT-based modeling defined nonarthritic glenoid subchondral bone surface and internal structure. First and second principal shape components were related to size and density. Reproducible structural parameters and glenoid feature relationships were discovered. Subchondral bone surface was approximated by a circle inferiorly and a triangle superiorly with the circle's center at a reproducible point along a superior-inferior line. Glenoid vault's maximum depth was at the circle's center, and the highest bone density was in posterior glenoid. Glenoid subchondral bone surface version varied from superior to inferior, but not by sex or side. Male subchondral bone surfaces were larger and more retroverted. Even if subchondral bone surfaces are deformed by arthritis, glenoid morphology can be identified by extra-articular landmarks, permitting location of the glenoid center and scapular orientation (glenoid version). Knowledge obtained from this study directs design of novel prosthesis features and instrumentation for use without pre-op CT or computer aided surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".