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. However, musculoskeletal complaints, including joint pain and stiffness, are common, and can lead to nonadherence and treatment discontinuation. The aim of this study was to characterize the natural history of the AI-induced arthralgia syndrome.Methods: Postmenopausal women with stage I-III breast cancer 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 worsening joint symptoms. Quality of life was assessed using the Functional Assessment of Cancer Therapy-Endocrine Subscale (FACT-ES), where higher scores signify improved well-being. Hand grip strength was measured at each visit with a Martin dynamometer. Paired t-tests were performed to compare follow-up evaluations to baseline.Results: A total of 34 patients have been enrolled to date. Three-month data is available on 22; six-month data on 12. Median age: 60 (42-81); White/Black/Hispanic/Asian: 13/6/2/1; median BMI (kg/m2): 28 (20-43). Compared to baseline, there was a statistically significant increase in BPI pain severity and pain-related interference at 3 and 6 months. Significant changes in the M-SACRAH were seen as early as 3 months, but no difference in grip strength was detected. Participants reported significantly more endocrine-related symptoms on the FACT-ES at 3 and 6 months after initiating AI therapy.Conclusions: Treatment with adjuvant AI therapy is associated with significant worsening of joint pain and stiffness which was seen as early as 3 months and persisted at the 6 month evaluation. Understanding the natural history of this toxicity and validating appropriate measures to be used in interventions to prevent and treat AI-induced arthralgias are critical. Baseline3 Months6 Months Mean (SD)Mean (SD)P-value*Mean (SD)P-value*BPI-SF Pain Severity (0-10)2.66 (2.52)3.85 (2.48)0.053.96 (2.42)0.02Pain Interference (0-10)1.40 (1.95)3.25 (2.53)0.022.60 (2.51)0.01M-SACRAH Pain (0-200)47 (46)74 (52)0.0373 (65)0.08Stiffness (0-200)51 (45)85 (54)0.0383 (54)0.01Function (0-800)59 (74)129 (143)0.14168 (190)0.05WOMAC Pain (0-500)67 (95)110 (126)0.19113 (135)0.06Stiffness (0-200)29 (40)45 (54)0.4643 (56)0.06Function (0-1700)271 (324)445 (400)0.15468 (373)0.06FACT-ES Functional (0-28)20 (7)20 (6)0.9619 (6)0.15Physical (0-28)22 (5)20 (6)0.4420 (5)0.32Endocrine (0-72)58 (9)52 (14)0.0451 (11)0.01* Based on paired t-test, as compared to baseline. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 5044.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".