The accordion technique did not improve bone healing in a mouse model of distraction osteogenesis
Bibliographic record
Abstract
Distraction osteogenesis (DO) is a valuable surgical method for limb lengthening and bone defect correction, but its lengthy consolidation phase presents challenges. The accordion technique (AT), involving compression and distraction of bone segments, has shown potential for enhancing healing. This study aimed to investigate the effectiveness of the AT conducted at three different time points (distraction phase, early consolidation phase, or late consolidation phase) compared to conventional DO in a mouse osteotomy model. Healing was evaluated using in vivo microCT, histology, and computational modeling. Results showed that bridging frequency, BV, and callus tissue composition were similar between conventional DO and late consolidation AT. In contrast, distraction phase AT led to delayed healing at day 15 with a 72% reduction in BV compared to DO, but no significant differences by the endpoint. Early consolidation AT showed significantly impaired healing compared to DO, with only 29% of mice achieving bony bridging, and significantly reduced bone marrow area of the endpoint callus. In silico modeling was generally predictive of in vivo findings and suggested that application of the AT during early consolidation results in destruction of newly-formed vascular tissue. Overall, no benefit was observed for the AT compared to conventional DO with the parameters employed in this study.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".