Patient-Specific Implants and Fat Grafting for Contour Deformities Post Craniosynostosis Reconstruction: A Therapeutic Approach
Bibliographic record
Abstract
BACKGROUND: Contour deformities after fronto-orbital advancement for craniosynostosis reconstruction are commonly encountered. There is a paucity of literature describing secondary procedures to correct such deformities with reported outcomes. An approach to defect analysis and procedure selection is lacking. The authors present our experience utilizing fat grafting (FG) and patient-specific implant (PSI) reconstruction as management strategies for this population. METHODS: A retrospective analysis of consecutive patients who underwent secondary onlay PSI or FG for contour deformities after primary craniosynostosis reconstruction was carried out. Patient demographics, defect analysis, surgical approach, postoperative complications, and esthetic outcomes were recorded. Data were pooled across the entire cohort and presented in a descriptive manner. RESULTS: Fourteen patients (36% syndromic and 64% isolated) were identified that either underwent PSI (n = 7) with a mean follow-up of 56.3 weeks, FG (n = 5) with a mean follow-up of 36 weeks or a combination of both (n = 2) for deformities postcraniosynostis surgery. Supraorbital retrusion and bitemporal hollowing were the most common deformities. There were no intraoperative or postoperative complications. All patients achieved Whitaker class I esthetic outcomes and there were no additional revisions during the defined study period. The presence of a significant structural component or large (>5 mm) depression typically necessitated a PSI. CONCLUSIONS: Contour deformities after fronto-orbital advancement reconstruction can be successfully managed using FG and PSI either as a combination procedure or in isolation. The authors have proposed anatomical criteria based on our experience to help guide procedure selection. Future prospective studies would be beneficial in providing more objective assessment criteria.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".