Accuracy of Orbital Shape Reconstruction—Comparative Analysis of Errors in Implant Shape Versus Implant Positioning: A Cadaveric Study
Bibliographic record
Abstract
INTRODUCTION: Orbital blowout fractures are commonly reconstructed with implants shaped to repair orbital cavity defects, restore ocular position and projection, and correct diplopia. Orbital implant shaping has traditionally been performed manually by surgeons, with more recent use of computer-assisted design (CAD). Accuracy of implant placement is also key to reconstruction. This study compares the placement accuracy of orbital implants, testing the hypothesis that CAD-shaped implants indexed to patient anatomy will better restore orbit geometry compared with manually shaped implants and manually placed implants. METHODS: The placement accuracy of orbital implants was assessed within a cadaveric blowout fracture model (3 skulls, 6 orbits) via 3-dimensional CT analysis. Defects were repaired with 4 different techniques: manually placed-manually shaped composite (titanium-reinforced porous polyethylene), manually placed CAD composite, indexed placed CAD composite, and indexed placed CAD titanium mesh. RESULTS: Implant placement accuracy differed significantly with the implant preparation method ( P =0.01). Indexing significantly improved the placement accuracy ( P =0.002). Indexed placed titanium mesh CAD implants (1.42±0.33 mm) were positioned significantly closer to the intact surface versus manually placed-manually shaped composite implants (2.12±0.39 mm). DISCUSSION: Computer-assisted design implants indexed to patient geometry yielded average errors below the acceptable threshold (2 mm) for enophthalmos and diplopia. This study highlights the importance of adequately indexing CAD-designed implants to patient geometry to ensure accurate orbital reconstructions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".