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 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 | 0.000 | 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 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".