Alloplastic Cranioplasty Reconstruction
Bibliographic record
Abstract
BACKGROUND: Acquired defects of the cranium represent a reconstructive challenge in patients with calvarial bone loss due to trauma, infection, neoplasia, congenital malformations, or other etiologies. The objective of this study was to compare postoperative rates of infection, local complications, and allograft failures following cranioplasty reconstruction using titanium mesh (Ti), polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), and Norian implants in adult patients. METHODS: This constitutes the first systematic review of available literature on 4 different methods of alloplastic cranioplasty reconstruction, including Ti, PMMA, PEEK, and Norian implants, using the Newcastle-Ottawa Quality Assessment Scale guidelines for article identification, screening, eligibility, and inclusion. Electronic literature search included Ovid MEDLINE/PubMed, EMBASE, Scopus, Google Scholar, and Cochrane Database. Pearson exact test was utilized at P < 0.05 level of significance (J.M.P. v11 Statistical Software). RESULTS: A total of 53 studies and 3591 patients (mean age, 40.1 years) were included (Ti = 1429, PMMA = 1459, PEEK = 221, Norian = 482). Polymethyl methacrylate implants were associated with a significantly higher infection rate (7.95%, P = 0.0266) compared with all other implant types (6.05%). Polyether ether ketone implants were associated with a significantly higher local complication rate (17.19%, P = 0.0307, compared with 12.23% in all others) and the highest ultimate graft failure rate (8.60%, P = 0.0450) compared with all other implant types (5.52%). CONCLUSIONS: This study qualifies as a preliminary analysis addressing the knowledge gap in rates of infection, local surgical complication, and graft failure in alloplastic cranioplasty reconstruction with different implant types in the adult population. Longer-term randomized trials are warranted to validate associations found in this study.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".