Micro-CT evaluation of bone defects: Applications to osteolytic bone metastases, bone cysts, and fracture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bone defects can occur in various forms and present challenges to performing a standard micro-CT evaluation of bone quality because most measures are suited to homogeneous structures rather than ones with spatially focal abnormalities. Such defects are commonly associated with pain and fragility. Research involving bone defects requires quantitative approaches to be developed if micro-CT is to be employed. In this study, we demonstrate that measures of inter-microarchitectural bone spacing are sensitive to the presence of focal defects in the proximal tibia of two distinctly different mouse models: a burr-hole model for fracture healing research, and a model of osteolytic bone metastases. In these models, the cortical and trabecular bone compartments were both affected by the defect and were, therefore, evaluated as a single unit to avoid splitting the defects into multiple analysis regions. The burr-hole defect increased mean spacing (Sp) by 27.6%, spacing standard deviation (SpSD) by 113%, and maximum spacing (Spmax) by 72.8%. Regression modeling revealed SpSD (β=0.974, p<0.0001) to be a significant predictor of the defect volume (R(2)=0.949) and Spmax (β=0.712, p<0.0001) and SpSD (β=0.271, p=0.022) to be significant predictors of the defect diameter (R(2)=0.954). In the mice with osteolytic bone metastases, spacing parameters followed similar patterns of change as reflected by other imaging technologies, specifically bioluminescence data which is indicative of tumor burden. These data highlight the sensitivity of spacing measurements to bone architectural abnormalities from 3D micro-CT data and provide a tool for quantitative evaluation of defects within a bone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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