Thyroid Nodule Incidence, Characteristics, and Localization in Hemodialysis Patients With End‐Stage Renal Disease: A Cross‐Sectional Study in Palestine
Bibliographic record
Abstract
BACKGROUND: Patients with end-stage kidney disease (ESKD) undergoing hemodialysis frequently exhibit thyroid dysfunction or morphological abnormalities such as thyroid nodules. Although prior studies indicate a high prevalence of thyroid abnormalities in ESKD patients, few have explored the specific incidence, types, and anatomical distribution of thyroid nodules in this population. PURPOSE: This study aimed to determine the prevalence, types, and anatomical distribution of thyroid nodules in ESKD patients on hemodialysis and to evaluate the relationship between hemodialysis duration and nodule occurrence. METHODS: A cross-sectional study was conducted at Beit Jala Hospital, Palestine, involving 200 ESKD patients receiving hemodialysis. Thyroid ultrasound was used to assess nodule presence, morphology, and distribution. Patient demographics, medical history, and dialysis duration were analyzed using statistical methods. RESULTS: Thyroid nodules were identified in 41.0% (n = 82) of participants, with no significant gender differences (p = 0.839). Solid nodules (58.5%) were more prevalent than cystic nodules (41.4%), and unilateral multinodular goiter (60.5%) was more common than bilateral multinodular goiter (39.5%), though these differences were not statistically significant. A significant correlation was observed between hemodialysis duration and nodule prevalence, with a higher incidence in patients on dialysis for ≥ 5 years (p = 0.003), suggesting prolonged dialysis may contribute to nodule formation. CONCLUSION: This study confirms a high prevalence of thyroid nodules in ESKD patients on hemodialysis, predominantly solid nodules, and unilateral multinodular goiter. The significant association between longer dialysis duration and increased nodule prevalence highlights the need for routine thyroid monitoring in this population. Further research is needed to elucidate underlying mechanisms and optimize clinical management of thyroid abnormalities in ESKD 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".