Comparative Efficacy of Radioiodine Therapy With Adjunctive Thionamides vs Either Treatment Alone in the Management of Graves Disease: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
CONTEXT: Antithyroid drugs (ATDs) and radioactive iodine (RAI) are the main nonsurgical modalities for Graves disease, but there is no agreement on the optimal approach for utilizing these 2 alternatives, either individually or in combination. OBJECTIVE: This systematic review and meta-analysis of randomized controlled trials (RCTs) aimed to evaluate the success (hypothyroidism/euthyroid) and failure (hyperthyroidism/relapse) of adjunctive (pre-RAI/post-RAI/both) and monotherapy with ATDs in Graves disease. METHODS: PubMed, CENTRAL, Scopus, and Web of Science were searched to December 2023 for RCTs on adjunctive ATDs with RAI or ATDs monotherapy in adults. Pooled relative risks were estimated using a random-effects model. Certainty of findings was assessed with GRADE. RESULTS: We included 21 RCTs (1914 participants). Adjunctive ATDs showed no significant effect on success (RR: 0.96, 95% CI: 0.91-1.01) or failure (RR: 1.17, 95% CI: 0.97-1.42) rate (moderate GRADE) but increased the chance of euthyroid state and reduced hypothyroidism (RR: 0.67, 95% CI: 0.50-0.90) compared to RAI. ATDs monotherapy vs RAI with adjunctive ATDs showed no significant difference in resolving hyperthyroidism (RR: 0.93, 95% CI: 0.84-1.02). Propylthiouracil with RAI was associated with reduced likelihood of success (RR: 0.81, 95% CI: 0.69-0.96). CONCLUSION: ATDs do not significantly affect the success or failure rates of RAI therapy, particularly in long-term follow-up, but they may improve euthyroid outcomes and reduce hypothyroidism. Long-term ATD monotherapy showed no significant difference compared with RAI in resolving hyperthyroidism; however, additional long-term trials are needed to confirm these findings. Management of Graves disease requires individualized physician-patient decisions.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.025 | 0.042 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".