Incidence of Alemtuzumab-Induced Thyroid-Associated Orbitopathy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Alemtuzumab is a monoclonal antibody that targets CD52 and is commonly used to treat multiple sclerosis. Thyroid dysfunction occurs in 20% to 30% of patients treated with alemtuzumab. This may lead to thyroid-associated orbitopathy (TAO), which can cause debilitating dry eye, diplopia, proptosis, ocular pain, and vision loss. This meta-analysis aims to quantify the incidence of alemtuzumab-induced TAO (AI-TAO) and to characterize its clinical features. METHODS: Studies were extracted from Cochrane, Embase (Ovid), Medline (Ovid), and additional gray literature. Using R version 4.4.1 on RStudio, the meta-analysis was conducted using the meta package. Depending on the level of heterogeneity, either a fixed-effects or random-effects model was used to pool the data. Funnel plots were used to assess publication bias. RESULTS: Meta-analysis of 1545 patients across 12 studies revealed that the incidence of alemtuzumab-induced Graves' disease was 25% (95% confidence interval [CI]: 11-46%) and AI-TAO was 6% (95% CI: 3-10%). Of the patients with established alemtuzumab-induced Graves' disease, 20% (95% CI: 12-30%) developed TAO. Pooled analysis of 8 studies (n = 556), revealed that the mean onset of AI-TAO was 37.38 months (95% CI: 28.90-46.76). In summary, 51/65 (78.5%) of TAO patients were managed conservatively, and 22/65 (33.9%) were managed surgically. Orbital decompression was required in only 5/65 (7.7%) patients. CONCLUSIONS: The incidence of AI-TAO is 6% which is less common than the estimated incidence of 20% to 30% of alemtuzumab-induced thyroid dysfunction. This finding emphasizes the need for patient counseling, baseline ophthalmic examination, and interdisciplinary follow-up for early detection and management of AI-TAO.
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How this classification was reachedexpand
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".