Endocrine Effects of Tamoxifen Plus Exemestane in Postmenopausal Women with Breast Cancer
Bibliographic record
Abstract
PURPOSE: In some specific circumstances, combined hormonal therapies for breast cancer seem to be more effective than single maneuvers. In two laboratory mammary cancer models, the combination of the aromatase inactivator exemestane plus tamoxifen gives a higher response rate than is found with either agent alone. To evaluate the endocrine effects of the combination of exemestane and tamoxifen, we studied 33 postmenopausal women disease-free following primary treatments for breast cancer who were taking tamoxifen for at least 3 months. DESIGN: After observation for symptoms on tamoxifen for 4 weeks, blood samples were taken and patients were begun additionally on exemestane 25 mg p.o. qd. Eight weeks later, blood samples were again taken, and exemestane was discontinued. RESULTS: A decrease in alkaline phosphatase was found with exemestane treatment (P = 0.06), whereas no change in osteocalcin level was observed. A decrease in high-density lipoprotein cholesterol level was found (P = 0.0025), whereas total cholesterol, low-density lipoprotein cholesterol and triglyceride levels showed no changes with exemestane treatment. Estradiol, estrone, and estrone sulfate levels decreased to immeasurable or very low levels with exemestane treatment (all P < 0.001). No significant changes in frequencies of common drug-associated side effects, such as vasomotor symptoms or weight change, were found. CONCLUSIONS: Based on the absence of adverse endocrine effects with the addition of exemestane to tamoxifen therapy observed in this study, further clinical evaluation of the efficacy of this combination is warranted.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".