Hydroxyapatite Orbital Implant Vascularization Assessed by Magnetic Resonance Imaging
Bibliographic record
Abstract
PURPOSE: To report hydroxyapatite (HA) implant enhancement patterns on magnetic resonance (MR) images at varying time intervals after implantation. METHODS: We retrospectively reviewed the records of 45 consecutive patients(from one author's practice) who underwent an MR imaging study 2 to 157 months after HA orbital implant placement. Implant fibrovascular ingrowth was assessed by analyzing the extent of implant enhancement seen on MR imaging. RESULTS Of 21 patients undergoing gadolinium-DTPA T1-weighted MR imaging 2 to 7 weeks after HA placement, 15 had enhancement limited to the implant rim (Grade I or less). Five patients had peripheral foci of enhancement (Grade II), and one patient had foci of enhancement extending to the center of the implant (Grade III). MR images obtained 9 to 15 weeks after HA insertion in all 14 patients had some degree of central enhancement (Grade III) and 11 had homogeneous enhancement throughout the implant (Grade IV or V). Seven patients in the homogeneous group were believed to have particularly intense enhancement patterns (Grade V). Of the 10 patients undergoing MR imaging from 31 to 69 weeks after surgery, 5 had Grade III enhancement and 5 had Grade IV enhancement. CONCLUSIONS: This study demonstrated consistent central HA orbital implant enhancement on MR imaging in the 9- to 15-week group and the >31-week postoperative group. HA orbital implant drilling and peg placement should be performed after central vascularization of the spherical implant has occurred. The results of this study support the principle of performing orbital implant drilling and peg placement at least 5 to 6 months after HA implant insertion.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".