Immunological Therapies Can Relieve Aromatase Inhibitor-Related Joint Symptoms in Breast Cancer Survivors
Bibliographic record
Abstract
OBJECTIVES: Aromatase inhibitors can cause joint symptoms. The purpose of this pilot study was to evaluate the feasibility of immunologic therapies for this kind of joint symptoms. METHODS: A total of 16 postmenopausal women with stage I-III breast cancer with joint symptoms related to Aromatase inhibitors were enrolled. They received immunologic therapies of thymosin α1 1.6 mg, twice a week for 4 weeks. Outcome measures included the Brief Pain Inventory-Short Form, Western Ontario and McMaster Universities Osteoarthritis index, and the Functional Assessment of Cancer Therapy-General quality of life measure. Interferon-gamma and interleukin-4 were determined to evaluate immunomodulatory activity. Paired Samples Test and linear regression analysis were used to statistics the outcome measures. RESULTS: From baseline to the end of treatment, patients reported improvement in the mean Brief Pain Inventory-Short Form worst pain scores (5.7-3.4, P < 0.001), pain severity (3.9-2.9, P = 0.01), and pain-related functional interference (4.2-1.8, P < 0.001), as well as the Western Ontario and McMaster Universities Osteoarthritis function subscale and Functional Assessment of Cancer Therapy-General physical well-being (P < 0.001 and P < 0.001, respectively). No adverse events were reported. The mean serum concentrations for secretion of interferon-gamma were significantly lower (P < 0.001); serum concentrations of interleukin 4 were higher (P = 0.02). CONCLUSION: Immunologic therapies could play a role in reducing Aromatase inhibitor- related joint symptoms in breast cancer survivors and affecting the immune system in powerful ways. The improvements of immune system were associated with aromatase inhibitor-related joint symptoms.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".