A Longitudinal Low Dose<i>μ</i>CT Analysis of Bone Healing in Mice: A Pilot Study
Bibliographic record
Abstract
Low dose microcomputed tomography (μCT) is a recently matured technique that enables the study of longitudinal bone healing and the testing of experimental treatments for bone repair. This imaging technique has been used for studying craniofacial repair in mice but not in an orthopedic context. This is mainly due to the size of the defects (approximately 1.0 mm) in long bone, which heal rapidly and may thus negatively impact the assessment of the effectiveness of experimental treatments. We developed a longitudinal low dose μCT scan analysis method combined with a new image segmentation and extraction software using Hounsfield unit (HU) scores to quantitatively monitor bone healing in small femoral cortical defects in live mice. We were able to reproducibly quantify bone healing longitudinally over time with three observers. We used high speed intramedullary reaming to prolong healing in order to circumvent the rapid healing typical of small defects. Bone healing prolongation combined with μCT imaging to study small bone defects in live mice thus shows potential as a promising tool for future preclinical research on bone healing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".