A prospective model of care for breast cancer rehabilitation : bone health and arthralgias
Bibliographic record
Abstract
Free to read Musculoskeletal health can be compromised by breast cancer treatment. In particular, bone loss and arthralgias are prevalent side effects experienced by women treated with chemotherapy and/or adjuvant endocrine therapy. Bone loss leads to osteoporosis and related fractures, while arthralgias threaten quality of life and compliance to treatment. Because the processes that lead to these musculoskeletal problems are initiated when treatment begins, early identification of women who may be at higher risk of developing problems, routine monitoring of bone density and pain at certain stages of treatment, and prudent application of therapeutic interventions are key to preventing and/or minimizing musculoskeletal sequelae. Exercise may be a particularly suitable intervention strategy because of its potential to address a number of impairments; it may slow bone loss, appears to reduce joint pain in noncancer conditions, and improves other breast cancer outcomes. Research efforts continue in the areas of etiology, measurement, and treatment of bone loss and arthralgias. The purpose of this review is to provide an overview of the current knowledge on the management and treatment of bone loss and arthralgias in breast cancer survivors and to present a framework for rehabilitation care to preserve musculoskeletal health in women treated for breast cancer.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".