Targeted biological therapies for Graves’ disease and thyroid‐associated ophthalmopathy. Focus on B‐cell depletion with Rituximab
Bibliographic record
Abstract
Based on experience from the treatment of other autoimmune diseases and because of the limitations imposed by existing therapeutic options for Graves' disease (GD) and thyroid-associated ophthalmopathy (TAO), rituximab (RTX) was recently proposed as a novel therapy option. Here, we summarize the rationale for using RTX; give an overview of the possible mechanisms of action; and give an account of its effects and side-effects when used in GD and TAO. Scant evidence, originating from only a few methodologically inhomogeneous studies, suggests that RTX may prolong remission for hyperthyroidism over that seen with antithyroid drugs, at least in mild GD. Furthermore, in patients with TAO, who are unresponsive to conventional immunosuppressive therapy, RTX seems efficacious. As we wait for larger-scale randomized studies, RTX, should be considered experimental and reserved for patients who do not respond favourably to conventional therapy. It is the first in what is likely to be a series of new and emerging treatments specifically targeting relevant components of the immune system. Further studies will hopefully lead to improved and better tailored, individualized therapy for GD and especially TAO.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".