Efficacy and Safety of intravenous monoclonal antibodies in patients with moderate-to-severe active Graves’ophthalmopathy: a systematic review and meta-analysis
Bibliographic record
Abstract
Backgrounds: The effects of various treatments on Graves' ophthalmopathy (GO) have been studied. As monoclonal antibodies (mAbs) have been proposed for the treatment of moderate to severe GO, direct comparisons between different mAbs are lacking.We therefore conducted this meta-analysis to objectively compare the efficacy and safety of intravenous mAbs. Methods: To identify eligible trials, references published before September 2022 were electronically searched in PubMed, Web of Science, Pubmed, Embase,Cochrane Library, CBM, CNKI,Wan-Fang and ICTRP databases.The Newcastle-Ottawa scale (NOS) and the Cochrane Risk of Bias Assessment Tool were used to assess the risk of bias of the original studies.The primary and secondary outcomes were the response and inactivation rates, with the secondary outcomes being the clinical activity score (CAS),the improvement of proptosis and diplopia improvement,and the adverse event rate. Publication bias was evaluated, along with subgroup and sensitivity analyses. Results: A total of 12 trials with 448 patients were included. The meta-analysis showed that TCZ (tocilizumab) was most likely to be the best treatment in terms of response according to indirect contrast, followed by TMB (teprotumumab) and RTX (rituximab).TCZ, followed by TMB and RTX, was also most likely to be the best treatment in terms of reducing proptosis. In terms of improving diplopia, TMB was most likely to be the best treatment, followed by TCZ and RTX.TCZ was the highest probability of safety, followed by RTX and TMB. Conclusions: Based on the best available evidence,TCZ should be the preferred treatment for moderate to severe GO.In the absence of head-to-head trials,indirect comparisons of treatments are routinely used to estimate the effectiveness of the treatments of interest. In addition,the optimal dose and potential mechanism of action of monoclonal antibodies remain to be established,and it is encouraging that the treatment paradigm for GO may change in the future.This study was designed in accordance with the Preferred Reporting Items for conducting Systematic Reviews and Meta-Analyses (PRISMA)(27). Systematic Review Registration: http://www.crd.york.ac.uk/prospero, identifier CRD42023398170.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".