NCIC CTG MAP.3: Symptoms and quality of life (QoL) among racial/ethnic minority women taking the aromatase inhibitor (AI) exemestane (EXE) for breast cancer risk reduction.
Bibliographic record
Abstract
6557 Background: Racial/ethnic minority postmenopausal women reported fewer side effects than whites on the aromatase inhibitor (AI) letrozole in MA.17. We present symptoms and QoL according to race and ethnicity in women taking EXE for BC prevention in MAP.3. Methods: Adjusted proportions ever experiencing symptoms or reporting worsened SF-36 domains (change scores from baseline decreased by ≥ -5 points) among racial minorities (M) and whites (W), and Hispanics (H) and non-Hispanics (non-H), were compared using logistic regression models adjusting for age, body mass index (BMI), country, marital status, education level and employment status. Results: Of 2285 women randomized to EXE, 2231 (97.6%) women had race information: 129 M (6.1%) and 2102 W (93.9%).Compared to W, M were significantly older, had higher BMI, had lower Gail scores, received more drugs for cardiovascular diseases, and were more likely to live in the United States.M women experienced fewer sweats (12% vs. 22%; p = 0.005) and less vaginal dryness (8% vs. 16%; p=0.03). No significant difference between W and M was seen in the proportion with worsened QoL on any SF-36 domain. Ethnicity was known for 2198 women on EXE (96.2%): 241 H (11.0%) and 1957 non-H (89%). The following were less frequent among H women: hot flashes (21% vs. 42%; p<0.0001); fatigue (14% vs. 24%; p=0.01); sweats (5% vs. 24%; p<0.0001); insomnia (6% vs. 11%; p=0.001); heartburn (5% vs. 16%; p=0.001); nausea (3% vs. 7%; p=0.02); arthritis (5% vs. 12%; p=0.02); depression (7% vs. 11%; p=0.005); back pain (2% vs. 10%; p=0.002); cough (4% vs. 11%; p=0.02); and vaginal dryness (4% vs. 17%; p<0.0001). Fewer H women reported worsening in SF-36 bodily pain (60 vs. 66%; p=0.02) but more reported worsening in SF-36 mental health (59% vs. 48%; p=0.02). Conclusions: Consistent with MA.17 findings, racial/ethnic minorities experienced fewer adverse events on AI. H women had less bodily pain but reported worsening mental health. These differences may be important for minority women contemplating EXE for BC prevention. Genotypic variations and pharmocogenomics that might account for these differences merit investigation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".