Medical ovariectomy in menopausal breast cancer patients with high testosterone levels: a further step toward tailored therapy
Bibliographic record
Abstract
Five years of adjuvant therapy with anti-estrogens reduce the incidence of disease progression by about 50% in estrogen receptor-positive breast cancer patients, but late relapse can still occur after anti-estrogens have been discontinued. In these patients, excessive androgen production may account for renewed excessive estrogen formation and increased risks of late relapse. In the 50% of patients who do not benefit with anti-estrogens, the effect of therapy is limited by de novo or acquired resistance to treatment. Androgen receptor and epidermal growth factor receptor overexpression are recognized mechanisms of endocrine resistance suggesting the involvement of androgens as activators of the androgen receptor pathway and as stimulators of epidermal growth factor synthesis and function. Data from a series of prospective studies on operable breast cancer patients, showing high serum testosterone levels are associated to increased risk of recurrence, provide further support to a role for androgens in breast cancer progression. According to the above reported evidence, we proposed to counteract excessive androgen production in the adjuvant setting of estrogen receptor-positive patients and suggested selecting postmenopausal patients with elevated levels of serum testosterone, marker of ovarian hyperandrogenemia, for adjuvant treatment with a gonadotropins-releasing hormone analogue (medical oophorectomy) in addition to standard therapy with anti-estrogens. The proposed approach provides an attempt of personalized medicine that needs to be further investigated in clinical trials.
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 | 0.001 |
| 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".