Densities of Orbital Fat and Extraocular Muscles in Graves Orbitopathy Patients and Controls
Bibliographic record
Abstract
PURPOSE: To investigate CT densities of orbital soft tissue volumes in patients with Graves orbitopathy (GO) and to compare these with the densities of controls. METHODS: Observational case series. Of 95 patients with GO and 150 controls, soft tissue volumes, mean densities, and ratios of fat volume to orbital volume and muscle volume to orbital volume were calculated with software. The 95% confidence intervals of the controls were used as reference values. The densities were plotted against age and volume ratios. For statistical analysis SPSS 16.00.2 was used. p values were calculated with the following tests: analysis of variance, Pearson correlation, Kruskal-Wallis, Mann-Whitney, and linear regression. RESULTS: The main outcome measurements were differences in orbital soft tissue densities. In GO patients the mean orbital fat density was significantly higher than in controls (p ≤ 0.001) and independent of age (p = 0.23). The mean extraocular muscle density of GO patients was within the range of controls and did not decrease with age (p = 0.16) as it did in controls (p ≤ 0.001). Mean fat density increased with decreasing fat volume (p = 0.001). Mean extraocular muscle density increased slightly with increasing muscle volume (p = 0.09). Muscle density correlated with fat density in both controls and GO patients. CONCLUSIONS: Orbital fat density in GO patients is significantly higher than in controls and negatively correlated to fat volume but positively correlated to muscle volume and muscle density.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".