Standardized Three-Dimensional Volumes of Interest with Adapted Surfaces for More Precise Subchondral Bone Analyses by Micro-Computed Tomography
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Bibliographic record
Abstract
Micro-computed tomography can be used to analyze subchondral bone features below treated cartilage defects in animal models. However, standardized methods for generating precise three-dimensional (3D) volumes of interest (VOI) below curved articular surfaces are lacking. The aims of this study were to develop standardized 3D VOI models adapted to the curved articular surface, and to characterize the subchondral bone specifically below a cartilage defect zone in intact and defect femoral trochlea. Skeletally mature rabbit distal femurs (N = 8 intact; N = 6 with acute debrided and microdrilled trochlear defects) were scanned by micro-computed tomography. Bone below the defect zone (3.5 mm width, 3.6 mm length, 1 mm deep) was quantified using simple geometric rectangular VOIs, and an optimized 3D VOI model with an adapted surface curvature, the Rectangle with Adapted Surface (RAS) model. In addition, a 250-μm-thick Curved-RAS model analyzed bone at three discrete subchondral levels. Simple geometric VOIs failed to analyze ~17% of the tissue volume, mainly near the top of the curved trochlear ridges. The RAS models revealed that after debridement and drilling, only 31% of the original bone remained within the VOI and bone loss was mainly accounted for by surgical debridement. Adapted surface VOIs are better than simple geometric VOI shapes for quantifying structural features of subchondral bone below a curved articular surface. Structural differences between the bone plate and cancellous bone were best captured using the smaller, depth-dependent Curved-RAS model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it