Establishing Orbital Floor Symmetry to Support Mirror Imaging in Computer-Aided Reconstruction of the Orbital Floor
Bibliographic record
Abstract
BACKGROUND: Surgical precision in the reconstruction of the orbital floor is crucial to functional visual and aesthetic outcomes. Increasingly, computer-aided design is being utilized to aid in precise preoperative planning by using the mirror images of the unaffected side. The authors aim to use 3-dimensional (3D) quantitative analysis to establish whether the native orbital floor topography is sufficiently symmetric to support this practice. METHODS: Ten high resolution head and neck computed tomography scans of patients without periorbital pathology were obtained. These were imported into a 3D medical image processing software and segmented to isolate bilateral orbital floors. Each native orbital floor was compared to the mirror image of the contralateral side by conformance map computation. Data collection included measures of 25% and 75% quartile, median, mean, standard deviation, and root-mean-square (RMS). RESULTS: The topographic analysis demonstrated a high degree of topographic conformance with a mean RMS of 0.58 ± 0.37 mm. Further volumetric analysis comparing the total orbital volume between each side also demonstrates a high degree of volumetric symmetry with a mean difference of 0.55 mL (P = 0.30). CONCLUSION: Comparison of the native orbital floor and the mirror image of the contralateral side by conformance map computation and volumetric analysis demonstrated a high degree of morphologic similarity. The native orbital floor topography provides optimal symmetry to support mirror imaging techniques used in orbital floor reconstruction.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".