Evaluation of Risk Factors for Malignancy in Patients With Thyroid Nodules
Bibliographic record
Abstract
Background: Despite thyroid nodules mostly have benign nature, there are always malignancy risks. Therefore, understanding the mechanisms influencing this potential malignant transformation is highly important in thyroid cancer prevention strategies. We aimed in this case-control study to investigate the relationship of anthropometric parameters, and insulin resistance with thyroid nodules and the malignancy risk in nodular thyroid patients. Methods: This single-center case-control study included 81 patients with thyroid nodules who were divided into two groups according to post-thyroidectomy pathology: the malignant group included 36 differentiated thyroid carcinoma patients and the benign group included 45 patients. We compared the anthropometric and radiological parameters, homeostasis model assessment of insulin resistance (HOMA-IR), and insulin levels between both groups. Results: We observed significant differences as regard HOMA-IR, insulin levels and waist circumference between both groups. Benign thyroid nodules volume correlated with weight, waist, body mass index (BMI), fat percentage, age, HOMA-IR, and insulin levels. Malignant thyroid nodules volume did not correlate with any parameters apart from weight. We found that the final significant risk factor was HOMA-IR in stepwise logistic regression analysis. Conclusions: Malignant thyroid nodules are associated with higher insulin resistance, visceral obesity, and hyperinsulinemia. Furthermore, higher HOMA-IR is a significant risk factor for differentiated thyroid carcinoma. J Endocrinol Metab. 2022;12(2):66-72 doi: https://doi.org/10.14740/jem770
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".