Gynecologic Symptoms among Hormone Receptor-Positive Breast Cancer Patients on Oral Endocrine Therapy: A Cross-Sectional Study
Bibliographic record
Abstract
Endocrine therapy (ET) for hormone receptor-positive (HR+) breast cancer can contribute to gynecologic symptoms (GS) that impact vaginal health, sexual function, and quality of life (QoL). A cross-sectional study was conducted at St. Michael's Hospital in Toronto, Canada between July 2017 and June 2018 to examine the occurrence and frequency of GS among HR+ breast cancer patients on ET, patient-provider communication, female sexual dysfunction (FSD), and QoL. A Treatment Experience questionnaire was developed for this study and the Female Sexual Function Index (FSFI) and Menopause-Specific Quality of Life questionnaire (MENQOL) were also administered. Of 151 patients surveyed, 77 (51.0%) were on tamoxifen and 74 (49.0%) on an aromatase inhibitor. Most patients (84.1%, 95% confidence interval [CI] 77.3% to 89.5%) experienced at least one GS "all the time" or "often", or one or more infections, in the past year. Only 44 (31.9%) patients reported that their oncologist had ever previously asked them about experiencing GS. The prevalence of FSD was 61.2% (95% CI 46.2% to 74.8%) among 49 sexually active patients that completed the FSFI. Symptoms captured in the MENQOL's vasomotor domain were deemed most bothersome. Side effect management and patient-provider communication should be prioritized to optimize GS, vaginal health, and sexual function of ET users.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".