Management of aromatase inhibitor-associated musculoskeletal symptoms: A systematic review.
Bibliographic record
Abstract
157 Background: Aromatase inhibitor-associated musculoskeletal symptoms (AIMSS) are experienced by a significant number of women on adjuvant aromatase inhibitors for breast cancer. These symptoms can result in therapy noncompliance and there is no accepted standardised treatment. We believe that this is the first systematic review to consider the evidence for all pharmacological and non-pharmacological interventions in the treatment of AIMSS. Methods: We conducted a systematic search in PubMed, EMBASE, CINAHL and CENTRAL. Clinical trials and observational studies for all potential pharmacological and non-pharmacological interventions were included. Two reviewers screened the results and extracted the relevant data. Risk of bias for the full papers were assessed using JADAD or a modified Newcastle Ottawa score, with conference abstracts and letters to the editor excluded from this process. Results: Of 1260 records being identified, 887 unique citations were screened with 93 full text papers retrieved for assessment, and 37 selected for inclusion. We identified 9 pharmacological interventions (RCT = 3, pre-post = 4, cohort = 2) and 28 non-pharmacological interventions (RCT/CCT = 16, pre-post = 8, cohort = 3). The largest number of studies were for various exercise interventions (n = 7) with the largest trial showing a 29% decrease in worst joint pain scores compared to the control (2%, p < 0.001). The highest number of RCTs was for acupuncture (n = 5), the majority of which showed a significant benefit with both real and sham acupuncture. Other single RCTs showing benefit include the use of testosterone, calcitonin and etoricoxib. No study adequately controlled for contamination bias from extraneous variables of both pharmacological and non-pharmacological interventions. Conclusions: Given that pharmacological treatment is often recommended for AIMSS, it is surprising that there is limited published evidence for its use. Although the interventions being used appear tolerable with minimal adverse effects, the current level of evidence is low, and additional large RCTs with more rigorous controlling for contamination from other interventions are required to confirm some of the reported promising results.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".