Role of Supplementation with Selenium and Myo-inositol vs. Selenium alone in patients of Autoimmune Thyroiditis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background Autoimmune thyroiditis (AIT) is a condition characterized by inflammation of the thyroid gland. Hashimoto’s thyroiditis is a predominant type, involving the gradual destruction of the thyroid gland. It affects females more than males, with global prevalence of around 10–12%. Multiple studies imply that a combination of selenium and myo-inositol supplements can restore a euthyroid state in patients with auto-immune thyroiditis. The objective of this meta-analysis is to pool available evidence on effectiveness of supplementation therapy on treatment of AIT. Methods: Google scholar and PubMed databases were searched for randomized controlled trials (RCTs) and observational studies which reported outcomes of combined treatment in restoring a euthyroid state, specifically comparing it with selenium-only treatment. Changes in TSH, T3, T4, TPOAb, and TgAb levels from baseline were defined as indicators to compare the effect of combined vs. selenium only treatment in restoring euthyroid levels. The Cochrane risk of bias tool and Newcastle Ottawa Scale were used to assess the quality of the randomized control trials included in the study. For statistical analysis, Review Manager (version 5.4, Nordic Cochrane Centre, Copenhagen, Denmark) was used. Result: We pooled 3 studies enrolling 151 participants in MI + Se group and 137 participants in Se group. Supplementation of Se with MI demonstrated a significant reduction in TSH levels compared to Se alone (SMD= -1.15, 95% CI: -1.60 to -0.69, p < 0.00001). MI + Se treatment also significantly reduced TgAb levels compared to Se (SMD= -0.51, 95% CI: -0.78 to -0.24, p = 0.0002). In contrast, TPOAB, T3 and T4 levels were non-significantly reduced from baseline in patients treated with MI + Se when compared to Se alone (SMD= -0.81, 95% CI: -0.44 to 0.09, p = 0.20), (SMD = 0.16, 95% CI: -0.09 to 0.42, p = 0.22), and (SMD = 0.30, 95% CI: -0.23 to 0.83, p = 0.26) respectively. Conclusion: Supplementation of Se with MI showed a significant reduction in TSH and TgAb levels compared to selenium-only treatment, with non-significant reduction in TPOAB, T3 and T4 levels. This entails the need for powered clinical trials and observational studies with longer follow-ups to critically assess the role of combined therapy in restoring euthyroid state in patients with AIT.
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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 | 0.009 | 0.022 |
| 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 | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".