Grading Severity and Activity in Thyroid Eye Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it