Regional variations in cartilage thickness of the radial head; implications for prosthesis design
Bibliographic record
Abstract
Background Knowledge of potential variations in cartilage thickness is important in designing radial head implants. Objective To characterize the regional variations in radial head cartilage thickness. Methods Twenty‐seven cadaveric radii were dissected and scanned with computed tomography (CT) in neutral position. Three dimensional models were generated from CT scans and processed through a computer program by two independent observers. Cartilage thickness values were obtained at 41 predetermined landmarks located around the articular dish and side of the radial head. Results At the side of the radial head, cartilage in the posteromedial quadrant (0° to 90°) was significantly thicker than all other quadrants (p < 0.05). Cartilage thickness measurements within the articular dish were similar at a mean of 0.98 ± 0.13mm, but increased towards the rim of the radial head (p < 0.001). Regional variations of cartilage thickness within the rim circumference were also detected, with the thickest region located anteriorly (270°, p < 0.001) and thinnest region laterally (180°, p < 0.05). Conclusions Regional variations in cartilage thickness exist around the side of the radial head and within the articular dish. Such differences offer insight into the biomechanics of the elbow joint and may also affect overall radius length and dish depth, which may be clinically important for implant design. Funding: Departmental Grant Funding Source : Departmental
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".