Biomechanical Analysis of Hydroxyapatite Cement Cranioplasty
Bibliographic record
Abstract
A recent review of the authors' experience with hydroxyapatite (HA) cement cranioplasties revealed a high infection rate. During removal of these implants, all were loose and fractured. Forty percent of these patients had a history of minor trauma at the site of cranioplasty before experiencing infection. Minor trauma may fracture HA cranioplasties and result in infection. The purpose of this study is to determine the force to fracture full- and partial-thickness cranial defects reconstructed with HA cement and to compare peak loads of differing HA cement cranioplasty techniques. Standardized craniotomy defects were created in five fresh cadaver heads. Full-thickness defects were reconstructed with either rigid or flexible titanium mesh and then covered with HA cement. Partial-thickness defects were reconstructed with HA alone. After setting, a uniaxial impact was delivered to each of the defects. Peak loads were recorded, and defects were examined for evidence of fracture.Predictable fractures of the HA cranioplasties occurred at 1200 N in all full-thickness defects reconstructed with mesh and a thin layer of HA. Implant loosening and chipping was similar to what was seen clinically in the authors' patients with infections. Full-thickness defects in which titanium mesh was shaped like a cup and filled with a thick layer of HA resist fracture at 1200 N. Partial-thickness defects reconstructed with HA alone also do not fracture at this peak load. Patient selection, defect characteristics, and reconstructive techniques are factors that need to be considered before using HA cement for cranioplasty purposes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".