A 4D time‐lapse morphometry method to quantify bone formation and resorption during distraction osteogenesis
Bibliographic record
Abstract
Abstract Distraction osteogenesis (DO) is widely utilized for treating limb length discrepancy, nonunion, bone deformities and defects. This study sought to develop a 4D time‐lapse morphometry method to quantify bone formation and resorption in mouse femur during DO based on image registration of longitudinal in vivo micro‐CT scans. Female C57BL/6 mice (n = 7) underwent osteotomy, followed by 5 days of latency, 10 days of distraction and 35 days of consolidation. The mice were scanned with micro‐CT at Days 5, 15, 25, 35, 45, and 50. Histological sectioning and Movat Pentachrome straining were performed at Day 50. After registration of two consecutive micro‐CT images of the same bone (day x and day y), the spatially‐ and temporally‐linked sequences of formation, resorption and quiescent bones at the distraction gap were identified and bone formation and resorption rates (BFRdayx‐y and BRRdayx‐y) were calculated. The overall percentage error of the registration method was 2.98% ± 0.89% and there was a strong correlation between histologically‐measured bone area fraction and micro‐CT‐determined bone volume fraction at Day 50 (r = 0.89, p < 0.05). The 4D time‐lapse morphometry indicated a rapid bone formation during the first 10 days of the consolidation phase (BFRday15–25 = 0.14 ± 0.05 mm3/day), followed by callus reshaping via equivalent bone formation and resorption rates. The 4D time‐lapse morphometry method developed in this study allows for a continuous quantitative monitoring of the dynamic process of bone formation and resorption following distraction, which may offer a better understanding of the mechanism for mechano‐regulated bone regeneration and aid for development of new treatment strategies of DO.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".