Comparing CT to surface digitization‐based measurements of cartilage thickness
Bibliographic record
Abstract
Background Knowledge of cartilage thickness distribution is important for understanding the biomechanical properties of different joints. In recent studies, various techniques for measuring cartilage thickness have been assessed. Objective Compare the reliability of computed tomography (CT) and surface digitization‐based methods for measuring cartilage thickness. Methods Six cadaveric radii were CT scanned in air, barium sulfate, and iopamidol to determine which medium enabled optimal cartilage visualization. Images of cartilage in the best medium were used to generate three dimensional (3D) models for measurement. The radial heads were then digitized using an infrared motion tracking system. Cartilage thickness was measured as the distance from the deepest point on the cartilage surface to the bone. Both CT‐ and surface digitization‐based values were compared to physical measurements acquired using a digital caliper to determine which technique was most reliable for assessing cartilage thickness. Results CT and surface digitization had mean differences of −0.01±0.06mm (mean ± standard deviation) and 0.00±0.35mm, respectively. Surface digitization‐based measurements, however, had larger individual errors and a greater range of error (−0.42 to +0.62mm) compared to CT‐based measurements (−0.07 to +0.08mm). Conclusions CT is a more reliable technique for assessing cartilage thickness. 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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".