Orbital Decompression: Cadaver Study
Bibliographic record
Abstract
BACKGROUND: Patients with Graves' ophthalmopathy may need surgical treatment to alleviate ophthalmologic complications. The degree of reduction in proptosis following surgical intervention remains difficult to predict. OBJECTIVES: To elaborate a human model using cadaver orbits to study surgical management of Graves' ophthalmopathy. To evaluate quantitatively the contribution of each orbital wall decompression and their combinations in reduction in proptosis. To improve the ability to predict the degree of proptosis reduction according to the wall(s) chosen for decompression. METHODS: Artificial exophthalmos was created in 12 cadavers' orbits by injecting a polysaccharide gel in the peribulbar and retrobulbar tissues. Proptosis reduction was measured following successive orbital decompression. RESULTS: Decompression of one wall produced a nonstatistical significant reduction in proptosis. The combination of the medial and lateral walls significantly reduced the proptosis by a mean of 4.2 mm. Three-wall decompression gave a mean significant reduction of 6.6 mm, and when combined with the advancement of the lateral wall, it reduced proptosis by 12.5 mm. CONCLUSIONS: We created an experimental model for research and didactic purposes for surgical mangement of Graves' ophthalmopathy. With this model, to obtain 5 mm or more of proptosis reduction, three-wall decompression is required. Advancement of the lateral wall achieved a further reduction in proptosis. For a proptosis reduction of less than 5 mm, decompression of the medial and lateral walls is appropriate.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".