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 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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".