Evaluation of Postoperative Outcomes Following Early and Late Palate Repair: A Preclinical Study
Bibliographic record
Abstract
OBJECTIVE: To quantitatively assess the impact of early versus late surgical intervention on midfacial growth using a mouse model. METHODS: A full-thickness mucoperiosteal flap surgery was performed on newborn (P17) mice and on neonatal (P30) mice. High-resolution micro-computed tomographic imaging coupled with histomorphometric analyses was used to assess craniomaxillofacial growth. Histology and immunohistochemical analyses were used to assess cellular and molecular responses postsurgery. RESULTS: Early surgical intervention at P17 resulted in significant midfacial growth arrest, with pronounced maxillary hypoplasia. Histomorphometric analyses revealed significant ( P < 0.05) growth disruptions in the mid-palatal suture complex, including premature removal of the cartilaginous growth plate and its replacement by bone. In the suture itself, cell proliferation was significantly reduced ( P < 0.05) compared with controls. The same surgical intervention performed in mice at P30 did not lead to significant midfacial growth arrest. CONCLUSIONS: Early surgical intervention in a mouse model mirrors the adverse growth outcomes in children undergoing early cleft repair. Molecular and cellular observations accompanying this midfacial growth arrest may inform therapeutic strategies to mitigate midfacial growth disturbances in patients and highlight the need for refined surgical techniques to minimize adverse growth outcomes.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".