Bone Effects of Anti-Cancer Treatments in 2024
Bibliographic record
Abstract
Considerable progress has been made in the management of cancer patients in the last decade with the arrival of anti-cancer immunotherapies (immune checkpoint inhibitors) and targeted therapies. As a result, a broad spectrum of cancers, not just hormone-sensitive ones, have seen several patients achieve profound and prolonged remissions, or even cures. The management of medium- and long-term side-effects of treatment and quality of life of patients are essential considerations. This is especially true for bone, as bone fragility can lead to increased fractures and loss of autonomy, ultimately reducing the possibility of resuming physical activity. Physical activity is essential for lasting oncological remission and prevention of fatigue. While the issue of hormone therapies and their association with breast cancer has been recognized for some time, the situation is relatively new with regards to targeted therapies and immunotherapies. This is particularly challenging given the wide range of available targeted therapies and their application to numerous cancer types. This article provides a comprehensive review of the bone effects of the main anti-cancer therapies currently in use. The review goes beyond glucocorticoids and hormone therapies and discusses for each drug category what is known regarding cellular effects, BMD effects, and fracture incidence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".