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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".