Risk of Thyroid Nodular Disease and Thyroid Cancer in Patients with Acromegaly – Meta-Analysis and Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Acromegaly is a quite rare chronic disease caused by the increased secretion of growth hormone (GH) and subsequently insulin - like growth factor 1. Although cardiovascular diseases remains the most common cause of mortality among acromegalic patients, increased prevalence of malignant and benign neoplasms remains a matter of debate. The aim of this study is to evaluate the risk of thyroid nodular disease (TND) and thyroid cancer in patients with acromegaly. MATERIALS AND METHODS: PubMed, Cochrane Library, Scopus, Cinahl, Academic Search Complete, Web of Knowledge, PubMed Central, PubMed Central Canada and Clinical Key databases were searched to identify studies containing. Random-effects model was used to calculate pooled odds ratios and risk ratios of TND in acromegaly. Studies which not included control groups were systematically reviewed. RESULTS: TND was more frequent in acromegaly than in control groups (OR = 6.9, RR = 2.1). The pooled prevalence of TND was 59.2%. Also thyroid cancer (TC) proved to be more common in acromegalic patients (OR = 7.5, RR = 7.2), prevalence was 4.3%. The pooled rate of malignancy (calculated per patient) was equal to 8.7%. CONCLUSIONS: This study confirms that both TND and TC occur significantly more often in acromegalic patients than in general population. These results indicate that periodic thyroid ultrasound examination and careful evaluation of eventual lesions should be an important part of follow-up of patients with acromegaly.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 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".