Role of Body Composition in the Prediction of Skeletal Fragility Induced by Hormone Deprivation Therapies in Cancer Patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review paper is intended to show that changes in body composition are key in the pathogenesis of bone fragility amongst patients with breast and prostate cancer receiving hormone deprivation therapies (HDTs) and that the mechanism is based on the development of alterations in bone quality rather than in bone quantity. RECENT FINDINGS: Preclinical and clinical data suggest a tight connection amongst bone, adipose and muscular tissues by means of several soluble mediators, potentially leading to (1) bone resorption and bone quality deterioration in sarcopenic obese subjects, (2) bone mineral deposition in healthy trained subjects. Cancer patients treated with HDTs frequently fall into the first condition, named osteosarcopenic obesity. Current clinical guidelines for the prevention of treatment-induced osteoporosis focus on bone mineral density (BMD) as a main predictive factor for fracture risk; however, the pathophysiology underlying HDT-induced bone fragility differs from that of primary and postmenopausal osteoporosis, suggesting a prevalent role for bone quality alterations. Focusing on available data from clinical trials, in our review we suggest osteosarcopenic obesity as a common target for the prevention and treatment of HDTs-related metabolic and skeletal complications, beyond a BMD-centred approach.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".