Risk Factors for Titanium Mesh Implant Exposure Following Cranioplasty
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Bibliographic record
Abstract
PURPOSE: Titanium mesh is used to reconstruct the neurocranium in cranioplasties. Though it is generally well-tolerated, erosion of the overlying soft tissue with exposure of the implant is a complication that adversely affects patient outcomes. The purpose of this study is to investigate potential risk factors for titanium mesh exposure. METHODS: This study comprises all consecutive patients who underwent titanium mesh cranioplasty between January 2000 and July 2016. A retrospective chart review was conducted to extract demographics, details of management, and outcome. Latest postoperative computed tomography scans were reviewed to document the thickness of soft tissue coverage over the implant and the presence of significant extradural dead space deep to it. RESULTS: Fifty patients were included. Implant exposure occurred in 7 (14%), while threatened exposure was observed in 1 additional patient, for a total complication count of 8 (16%).Four (50%) exposure and 3 (7.1%) nonexposure patients underwent preoperative radiotherapy (odds ratio [OR] = 19.67, P = 0.018). Similarly, 4 (50%) exposure and 5 (11.9%) nonexposure patients had a free flap tissue transfer for implant coverage (OR = 6.50, P = 0.046). Postoperative computed tomography scans revealed significant thinning of soft tissues over titanium mesh in 7 (87.5%) exposure and 15 (35.7%) nonexposure patients (OR = 10.71 P = 0.040). No significant association was found between transposition/rotation flap, postoperative radiotherapy, or the presence of significant extradural dead space, and exposure (P = 0.595, P = 0.999, P = 0.44). CONCLUSION: Preoperative radiotherapy, free flap coverage, and soft tissue atrophy resulted in greater odds of titanium mesh exposure. The findings of this study provide important considerations for reconstructive surgeons using titanium mesh for cranioplasty.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 it