Risk Factors for Titanium Mesh Implant Exposure Following Cranioplasty
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".