A Comprehensive Review of Thyroid Eye Disease Pathogenesis: From Immune Dysregulations to Novel Diagnostic and Therapeutic Approaches
Bibliographic record
Abstract
Thyroid eye disease is a complex inflammatory disorder of the orbit that has gained tremendous interest over the past years, and numerous scientific efforts have been deployed to elucidate its pathophysiology for novel drug development. Our manuscript will delve into the molecular dysregulations involved in the pathogenesis of thyroid eye disease that led to its clinical manifestations. Abnormalities within the apoptotic pathway, inflammatory cascade, and autoimmune regulatory systems will be covered. We will further discuss the challenges involved in its diagnosis and management and provide a summary of the current diagnostic tools (i.e., molecular biomarkers, diagnostic scores) from the perspective of clinicians. Finally, our comprehensive literature review will provide a thorough summary of most recent preclinical and clinical studies around the topic of thyroid eye disease, with an emphasis on the manuscripts published within the last five years. We believe our manuscript will bring novelty within the field by bridging the fundamental sciences with the clinical aspect of this disease. This review will be a great tool for clinicians in better understanding the pathogenesis of thyroid eye disease while providing an outlook on future perspectives (i.e., liquid biopsies, artificial intelligence).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".