The Interplay Between Thyroid Disorders and Diabetes and Their Impact on Cardiovascular Outcomes: A Systematic Review
Bibliographic record
Abstract
Diabetes mellitus and thyroid diseases are closely related, as both conditions share pathophysiological pathways and risk factors that increase cardiovascular risk. The purpose of this research is to thoroughly examine the literature about the connection between thyroid conditions and diabetes mellitus and determine how these conditions affect cardiovascular outcomes. PubMed, Google Scholar, Science Direct, and BioMed Central (BMC) databases were analysed using keywords "thyroid dysfunction", "hypothyroidism", "hyperthyroidism", "diabetes", "diabetes mellitus", "cardiovascular illness", and "cardiovascular outcomes". A comprehensive search of databases yielded studies published between 2011 and 2024, focusing on human subjects and the interplay between thyroid function and diabetes. The articles selected are screened for selection. A total of 4205 papers were selected after screening. After duplicate removal, 1185 articles were selected and underwent review, and 12 articles were chosen for quality appraisal using the Newcastle-Ottawa Scale for cross-sectional studies. Eight articles qualified the criteria for selection and quality appraisal. There is a bidirectional relationship between thyroid dysfunction and diabetes, underscoring the need for comprehensive management to mitigate cardiovascular risk found in this review. Regular thyroid screening, monitoring of cardiovascular risk factors, and timely intervention are crucial. Future research should prioritise longitudinal studies, standardised protocols, and molecular insights to inform evidence-based practice and optimise patient outcomes.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".