Plasma Aromatase Activity Index, Gonadotropins and Estrone Are Associated with Frailty Syndrome in Post-Menopausal Women with Breast Cancer
Bibliographic record
Abstract
Frailty syndrome is associated with poor outcomes, morbidity and premature mortality. We performed a cross-sectional study to evaluate the presence of frailty syndrome based on Fried’s frailty phenotype in post-menopausal women with breast cancer. We further analyzed the association between frailty syndrome with geriatric assessments and the association with the concentration of gonadotropins LH and FSH, estrogens, androgens and the aromatase activity index in the blood. We enrolled 47 post-menopausal women with localized breast cancer (mean age 66.8 ± 1.3 years (range 52−83)) prior to the starting of adjuvant endocrine therapy. Patients were identified as “non-frail” (robust) or “prefrail/frail” if they fulfilled at least one frailty criteria. In order to determine associations among variables and to control for other variables potentially affecting frailty syndrome (age, comorbidity index and previous chemotherapy treatment), we performed a logistic regression analysis. The receiver operating characteristic curve was performed to assess the sensitivity and specificity of the hormonal concentration to discriminate prefrail/frail versus non-frail individuals. Significant positive associations were observed between the severity of frailty syndrome and estrone, FSH and LH concentrations and the aromatase activity index in the blood (p < 0.05). Further research into the role of hormonal biomarkers should be evaluated in follow-up studies in order to recommend their use as suitable biomarkers of frailty syndrome in breast cancer 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 | 0.000 | 0.002 |
| 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".