Bioengineered Materials for Skull Base Reconstruction—Current Clinical Applications: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Skull base reconstruction (SBR) is a crucial aspect of open and endoscopic skull base surgery. Currently, multilayer reconstruction with vascularized tissues is the standard technique. Despite advancements, complications such as postoperative cerebrospinal fluid leaks (PO-CSF-L) and infections persist. Bioengineered materials (BEM) have emerged for SBR, showing promising results. A systematic review was conducted using Embase, PubMed, Scopus, and Cochrane databases. We performed a proportional meta-analysis of studies utilizing BEM for SBR and a comparative analysis with control groups that underwent SBR without biomaterials. The odds ratio assessed treatment effects for binary outcomes. From 1,075 potential articles, 14 met the inclusion criteria. Five BEM were identified: hydroxyapatite (HXA), leukocyte–platelet-rich fibrin (L-PFR), collagen matrix (CM), polyglycolic acid (PGA), and porous polyethylene (PP). The analysis included 1,960 patients, with 1,570 in experimental groups using BEM. Pooled data indicated a PO-CSF-L proportion of 0.02% (95% CI: 0.01–0.03%), postoperative CSF diversion (PO-CSF-d) at 0.01% (95% CI: 0.00–0.04), and PO infection at 0.02% (95% CI: 0.00–0.05%). Common effect models showed that CM had a lower total PO infection rate (0.01; 95% CI: 0.00–0.01, p = 0.0006) compared with HXA (0.08; 95% CI: 0.05–0.11, p = 0.0007). Comparative analysis demonstrated lower odds of PO-CSF-L (OR 0.37; 95% CI: 0.15–0.89, p = 0.026) and infections (OR 0.47; 95% CI: 0.13–1.47, p = 0.264) in patients with BEM. Our results indicate that bioengineered materials are viable for skull base reconstruction, associated with low rates of postoperative CSF leaks, diversions, and infections.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".