Author response: Relationship between circulating FSH levels and body composition and bone health in patients with prostate cancer who undergo androgen deprivation therapy: The BLADE study
Bibliographic record
Abstract
Treatments given to cancer patients can cause negative side effects. For example, a treatment known as androgen deprivation therapy – which is used to reduce male sex hormone levels in prostate cancer patients – can lead to increased body fat percentage and decreased bone density. These adverse effects can have further negative impacts on patient health, such as increasing the risk of cardiovascular disease and fractures from falls from standing height or less, respectively. Understanding how androgen deprivation therapy contributes to these negative side effects may help clinicians better manage care and outcomes for patients with prostate cancer. Follicle stimulating hormone (or FSH for short) has roles in male and female reproduction but has also been linked to changes in body composition. For example, elevated FSH levels are associated with higher total fat body mass in post-menopausal women. While androgen deprivation therapy is known to alter FSH blood levels, the impact of this change in prostate cancer patients was not well understood. To investigate the effect of androgen deprivation therapy on FSH levels and body composition, Bergamini et al. used X-ray technology to measure total fat body mass in prostate cancer patients before and after undergoing 12 months of androgen deprivation therapy. The findings showed that patient FSH blood levels significantly decreased after 12 months of treatment. Higher FSH blood levels strongly correlated with increased total fat body mass after 12 months of treatment. The findings of this clinical trial suggest that FSH blood levels impact the body composition of patients undergoing androgen deprivation therapy. As a result, FSH blood levels may be a suitable biomarker for identifying patients that are more likely to develop obesity and are therefore at greater risk of complications such as cardiovascular disease.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.014 |
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".