Standardized Three-Dimensional Volumes of Interest with Adapted Surfaces for More Precise Subchondral Bone Analyses by Micro-Computed Tomography
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".