A critical evaluation of body composition modalities used to assess adipose and skeletal muscle tissue in cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The majority of cancer patients experience some form of body composition change during the disease trajectory. For example, breast cancer patients undergoing chemotherapy and prostate cancer patients undergoing androgen deprivation therapy gain fat and lose skeletal muscle, which are associated with increased risk of cancer recurrence and clinical comorbidities. In contrast, advanced cancer patients, such as lung and colorectal cancer patients, experience symptoms of cancer cachexia (accelerated loss of skeletal muscle with or without adipose tissue loss), which are associated with decreased treatment response and poorer survival rates in advanced cancers. The heterogeneity of body composition features and their diverse implications across different cancer populations supports the need for accurate quantification of muscle and adipose tissue. Use of appropriate body composition modalities will facilitate an understanding of the complex relationship between body composition characteristics and clinical outcomes. This will ultimately support the development and evaluation of future therapeutic interventions that aim to counter muscle loss and fat gain in cancer populations. Despite the various metabolic complications that may confound the accurate body composition measurement in cancer patients (i.e., dehydration may confound lean tissue measurement), there are no guidelines for selecting the most appropriate modalities to make these measurements. In this review we outline specific considerations for choosing the most optimal approaches of lean and adipose tissue measurements among different cancer populations. Anthropometric measures, bioelectrical impedance analysis, air displacement plethysmography, dual-energy X-ray absorptiometry, computed tomography, and magnetic resonance imaging will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it