Bibliographic record
Abstract
PURPOSE: Thyroid eye disease (TED) is an autoimmune disorder causing inflammation, expansion, and fibrosis of orbital fat, muscle, and lacrimal gland. This article reviews the different methods of grading severity and activity of TED and focuses on the VISA Classification for disease evaluation and planning management. METHODS: Accurate evaluation of the clinical features of TED is essential for early diagnosis, identification of high-risk disease, planning medical and surgical intervention, and assessing response to therapy. Evaluation of the activity and severity of TED is based on a number of clinical features: appearance and exposure, periorbital tissue inflammation and congestion, restricted ocular motility and strabismus, and dysthyroid optic neuropathy. The authors review these clinical features in relation to disease activity and severity. RESULTS: Several classification systems have been devised to grade severity of these clinical manifestations. These include the NO SPECS Classification, the European Group on Graves Orbitopathy severity scale, the Clinical Activity Score of Mourits, and the VISA Classification as outlined here. The authors compare and contrast these evaluation schemes. CONCLUSIONS: An accurate clinical assessment of TED, including grading of disease severity and activity, is necessary for early diagnosis, recognition of those cases likely to develop more serious complications, and appropriate management planning. The VISA Classification grades both disease severity and activity using subjective and objective inputs. It organizes the clinical features of TED into 4 discrete groupings: V (vision, dysthyroid optic neuropathy); I (inflammation, congestion); S (strabismus, motility restriction); A (appearance, exposure). The layout follows the usual sequence of the eye examination and facilitates comparison of measurements between visits and data collation for research.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".