Evaluation of the incidence rates of subclinical hypothyroidism and hypoparathyroidism in breast cancer patients undergoing radiotherapy
Bibliographic record
Abstract
Background: The current advances in radiotherapy (RT) have improved the outcome of breast cancer (BC) patients. Despite its therapeutic benefits, the iatrogenic toxicities of RT and its impact on BC survivors are still debated, and further evaluations should be considered. This study aims to assess the rate of subclinical hypothyroidism and hypoparathyroidism among BC patients who were exposed to therapeutic radiation. Methods: Seventy females undergoing RT for BC were enrolled in this cross-sectional study. Laboratory assessment of thyroid stimulating hormone (TSH), free thyroxine (fT4), and free triiodothyronine (fT3) levels was obtained to evaluate thyroid function. The parathyroid function was evaluated by measuring serum levels of Calcium (Ca), Phosphorus (P), and parathyroid hormone (PTH) at baseline, six and 12 months after RT. Results: The mean age of patients was 54.3±6.4 years. We found no cases of hypothyroidism before radiotherapy. However, nine patients developed hypothyroidism in the six months after radiotherapy (one clinical and eight subclinical, 13% in total), and six patients were identified with hypothyroidism in the 12 months after radiotherapy (one clinical and five subclinical, 8.7% in total). Significant relationships were observed in the hypothyroidism rate at both six months (p = 0.003) and 12 months (p = 0.028) after RT compared with the baseline. There was no case of hypoparathyroidism before and after RT. Conclusion: In summary, we found that thyroid and parathyroid dysfunction after RT are relatively common findings among women with BC. It is a treatable source of morbidity in patients undergoing RT. Therefore, routine thyroid function monitoring should be recommended to improve the quality of life in BC survivors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".