Dual treatment of acromegaly and hormone-receptor-positive breast cancer with tamoxifen: a case report
Bibliographic record
Abstract
BACKGROUND: Adjuvant endocrine therapy is recommended for the treatment of hormone-receptor-positive breast cancer. Aromatase inhibitors are associated with significant musculoskeletal adverse effects, likely through growth hormone/insulin-like growth factor 1 modulation, while tamoxifen reduces insulin-like growth factor 1 production. We describe the case of a patient who was treated successfully with tamoxifen for her hormone-receptor-positive breast cancer and acromegaly. CASE PRESENTATION: A 57-year old White female with hormone-receptor-positive breast cancer was diagnosed with acromegaly. She received adjuvant endocrine therapy with anastrozole but could not tolerate this medication because of severe arthralgia, so she was switched to tamoxifen. Shortly after starting tamoxifen, the patient's musculoskeletal symptoms resolved and her insulin-like growth factor 1 levels normalized. She has remained in remission of her acromegaly and breast cancer since initiating tamoxifen. CONCLUSION: This case highlights the dual benefit of tamoxifen therapy in the treatment of hormone-receptor-positive breast cancer and acromegaly. Unlike anastrozole, tamoxifen has the benefit of lowering insulin-like growth factor 1 levels, which underscores its advantage in reducing adverse musculoskeletal symptoms during the treatment of hormone-receptor-positive breast cancer. We offer the first reported use of tamoxifen monotherapy for the successful treatment of acromegaly and hormone-receptor-positive breast cancer. While tamoxifen may offer an additional, oral option for acromegaly patients who do not respond to or tolerate conventional growth-hormone-lowering therapy, additional studies are necessary.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".