Abstract P2-12-01: Prospective Evaluation of Joint Symptoms in Postmenopausal Women Initiating Aromatase Inhibitors for Early Stage Breast Cancer
Bibliographic record
Abstract
Abstract Background: Aromatase inhibitors (AIs) are widely prescribed to postmenopausal women for the adjuvant treatment of hormone-sensitive breast cancer (BC). However, musculoskeletal complaints can lead to nonadherence and early discontinuation. The aim of this study was to characterize the natural history of the AI-induced arthralgia syndrome and determine predictors of worsening symptoms. Methods: Postmenopausal women with stage I-III BC initiating adjuvant AI therapy were enrolled. All patients completed the following questionnaires at baseline and every 3 months for a year: Modified Brief Pain Inventory-Short Form (BPI-SF), Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index and the Modified Assessment of Chronic Rheumatoid Affections of the Hands (M-SACRAH). Higher scores reflect worse symptoms. Quality of life was assessed using the Functional Assessment of Cancer Therapy-Endocrine Subscale (FACT-ES). Hand grip strength was measured at each visit with a Martin dynamometer. Paired t-tests were performed to compare follow-up evaluations to baseline. Linear Regression was performed to evaluate the association between baseline symptoms and change in symptoms. Results: A total of 169 patients have been consented, 3-month data is available on 102; 6-month data on 85. Median age: 63 (42–89); White/Black/Asian/Hispanic: 61.48/30.33/3.28/25.6; median BMI (kg/m2): 28 (12–50). Compared to baseline, there was a statistically significant increase in BPI pain severity and endocrine related symptoms on the FACT-ES at 3 and 6 months. Significant changes in the BPI pain interference, M-SACRAH pain and stiffness, WOMAC function, physical well-being on the FACT-ES, trial outcome index and pinch grip strength were seen at 3 months; however these changes did not remain significant at 6 months. Logistic regression models evaluating predictors of patient reported outcome measures and grip strength were performed. Baseline score was the strongest predictor of worsening symptoms (p < 0.01) after correcting for age, prior chemotherapy and baseline joint conditions. Conclusions: Treatment with adjuvant AI therapy is associated with significant worsening of joint pain and stiffness which is most prominent at the 3 months evaluation. Patients with baseline joint symptoms are at greatest risk for worsening symptoms on AIs. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-12-01.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 teacher head, 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".