The Prevalence of Hypothyroidism among Patients With β-Thalassemia: A Systematic Review and Meta-Analysis of Cross-Sectional Studies
Bibliographic record
Abstract
As a cause of chronic blood transfusions, iron overload is an important issue in β-thalassemia (β-thal) patients that leads to multiple organ dysfunctions. This is an updated meta-analysis conducted to summarize the existing evidence of the prevalence of hypothyroidism (HT) among patients with transfusion-dependent (TDT) and non transfusion-dependent β-thal (NTDT) and for the first time we meta-analyzed the relationship between ferritin level and HT. This systematic review and meta-analysis were done according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. We searched databases including Web of Science (ISI), Scopus, PubMed, Embase, and Scholar. The quality of the included studies was assessed based on the Newcastle-Ottawa scale (NOS) checklist. Meta-analysis was done using Stata statistical software. The pooled prevalence of total HT, subclinical HT, and overt HT among β-thal patients was 13.25 [95% confidence interval (95% CI): 10.29–16.21; 11.84, 95% CI: 8.43–15.25 and 12.46, 95% CI: 1.05–23.87], respectively. The prevalence of total HT was 16.22% (95% CI: 12.36–20.08) in TDT and 7.22% (95% CI: 3.66–10.78) in NTDT patients. Serum ferritin (SF) levels were significantly lower in euthyroid compared to hypothyroid patients [standard mean difference (SMD) −2.15 (95% CI: −3.08, −1.21, p value <0.001]. The prevalence of HT was higher in TDT compared to NTDT patients. Moreover, our results showed a significant association of high serum ferritin (SF) levels with hypothyroidism in β-thal patients. Both of these findings highlight the importance of prevention measures and timely diagnosis and management of iron overload in β-thal patients.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.037 |
| Bibliometrics | 0.006 | 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 | 0.001 |
| 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".