Hydroxyapatite Implant Wrapping Materials: Analysis of Fibrovascular Ingrowth in an Animal Model
Bibliographic record
Abstract
PURPOSE: To compare the influence of seven currently available spherical orbital implant wraps on host fibrovascularization of a hydroxyapatite (HA) orbital implant. METHODS: Five groups of 3 (15 total) adult male New Zealand albino rabbits underwent enucleation with placement of a 12-mm HA implant wrapped in high-porosity expanded polytetrafluoroethylene (e-PTFE), processed bovine pericardium, or processed human pericardium, sclera, or fascia lata. Magnetic resonance imaging before and after the intravenous administration of gadolinium-diethylenetriamine pentaacetic acid (DTPA, 0.1 mol/kg) was performed immediately before exenteration. Five rabbits (one with each of the different implant wraps) were killed at 4, 8, and 12 weeks, and the operated socket was exenterated. Histopathologic sections of the implants were then compared with the results of our previous study using polyglactin 910 mesh and autologous sclera as HA orbital implant wraps. RESULTS: Complete fibrovascularization of all the implants occurred by 12 weeks; however, HA implants wrapped with sclera, polyglactin mesh, and e-PTFE appeared to undergo more rapid fibrovascularization than spheres wrapped with other materials. CONCLUSIONS: Although all of the implant wraps studied may be suitable substitutes for donor sclera, we prefer polyglactin mesh because it is readily available, inexpensive, and without risk of transmissible diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".