Hypogonadism In Cancer Cachexia : Effect On Symptoms And Body Composition
Bibliographic record
Abstract
Introduction: Hypogonadism is common among men with advanced cancer. Little is known about the impact of hypogonadism on symptoms and body composition in cancer cachexia.Methods: A retrospective chart review of men referred to a Cancer Rehabilitation Program was performed. All men had bioavailable testosterone (BT) measured. Patients were classified as hypogonadic (hypo) or eugonadic (eugo) based on age-specific cutoffs. Additionally, patients were identified as cachectic (C), as defined by Vigano et al. (Clin Nutr. 36:1378-1390, 2017). Body composition was measured using dual-energy X-ray absorptiometry; appendicular skeletal muscle index (ASMI) was calculated. The revised Edmonton Symptom Assessment System questionnaire (ESAS-r) was completed. Men who had prostate or testicular cancers were excluded. Results: Eighty patients were included (mean age 67.2u00b110.2 y). The overall prevalence of hypogonadism was 31.2%. When comparing the eugo+C and hypo+C patients respectively, ESAS-r scores for appetite (4.78u00b13.34 vs 6.54u00b12.43; p=0.02) and wellbeing (4.66u00b12.50 vs 6.37u00b12.51; p=0.006) were significantly greater in hypo+C patients. In addition, fatigue exhibited a statistical trend between the two groups (5.20u00b12.54 vs 6.29u00b12.17; p=0.07). No difference in ASMI was found between the eugo+C versus the hypo+C patients (6.27u00b10.87 vs 6.50u00b10.75 kg/m2; p=0.834); both groups fell far below the cutoff (7.26 kg/m2) for normal muscle mass. Conclusion: Male hypogonadism may negatively impact quality of life and cancer symptoms in cancer cachexia. In cachectic patients, male hypogonadism does not seem to have a clinically significant impact on muscle mass. Testosterone replacement therapy may be of greater use for preserving quality of life, rather than muscle mass, in cachexia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".