The Role of Diuretics in Treatment of Aromatase Inhibitors Induced Musculoskeletal Symptoms in Women with Non Metastatic Breast Cancer
Bibliographic record
Abstract
Background: Around 50% of women receiving Aromatase Inhibitors (AIs) develop musculoskeletal symptoms which may become severe causing interruption of treatment. Patients with AI-induced arthralgia had higher rates of joint effusions and fluid in the tendons, so use of diuretics may be helpful. Methods: This prospective phase II study was conducted in department of clinical oncology and nuclear medicine, Menoufia University Hospital, Egypt, between Jan. to Dec. 2015. Fifty Women with stage I,II and III breast cancer receiving AIs as adjuvant hormonal treatment complaining of AIs related musculoskeletal symptoms received Lasilactone® 50 mg tablet; (an oral combination of Frusemide 20mg/Spironolactone 50 mg), every other day for 4 weeks. Patients were assessed by modified Western Ontario and McMaster Universities osteoarthritis (WOMAC) index for lower limb and the quick Disabilities of the Arm, Shoulder and Hand Score (DASH) scoring system for upper limbs, Arabic versions, at baseline and after 4 weeks of treatment. Results: The mean WOMAC pain score improved significantly (6.0 v 10; P < 0.001), the mean WOMAC stiffness score improved (2.3 v 3.9; P = 0.002), the mean WOMAC functional score improved (8.7 v 15; P < 0.001), the total WOMAC score improved (17 v 29; P < 0.001), also a significant difference was noticed for the quick DASH score; total score (16 v 25; P = 0.02) After use of diuretics for 4 weeks of treatment compared with baseline scores. Conclusions: The use of diuretics effectively reduces AI related musculoskeletal symptoms with good tolerance.
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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 | 0.001 |
| 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".