Bibliographic record
Abstract
For the upfront adjuvant therapy of postmenopausal estrogen receptor-positive breast cancer, the third-generation aromatase inhibitors (AIS) have shown a more favourable overall risk-benefit profile than has tamoxifen. Benefits of the ais include less frequent gynecologic, cerebrovascular, and thromboembolic adverse events; greater disease-free survival; and lower tumour recurrence. Although approximately 25% of postmenopausal women with early breast cancer report experiencing symptoms of arthralgia with ai therapy, 68-month data from the Arimidex, Tamoxifen, Alone or in Combination trial showed that, compared with tamoxifen, anastrozole treatment was associated with only a modest increase in the incidence of joint symptoms. The events, which were mostly mild-to-moderate in intensity, led to treatment withdrawal in 2% of patients on anastrozole as compared with 1% in the tamoxifen arm. The symptoms and changes correlate with clinical, biochemical, and radiologic findings in symptomatic women. To determine appropriate intervention, it is therefore essential to perform a comprehensive evaluation of musculoskeletal complaints to distinguish natural menopause-related degenerative disease from AI-related effects. The present review explores the advantages of differential diagnosis with an emphasis on history and physical and musculoskeletal examination; laboratory investigations are used to corroborate or rule out clinical impressions. The transient symptoms associated with the ais are manageable with an appropriate combination of lifestyle changes, including exercise and joint protection in conjunction with pharmacologic approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".