Sarcopenia and cachexia in the era of obesity: clinical and nutritional impact
Bibliographic record
Abstract
Our understanding of body composition (BC) variability in contemporary populations has significantly increased with the use of imaging techniques. Abnormal BC such as sarcopenia (low muscle mass) and obesity (excess adipose tissue) are predictors of poorer prognosis in a variety of conditions or clinical situations. As a catabolic illness, a defining feature of cancer is muscle loss. Although the conceptual model of wasting in cancer is typically conceived as involuntary weight loss leading to low body weight, recent studies have shown that both sarcopenia and cachexia can be present with obesity. The combination of low muscle and high adipose tissue (sarcopenic obesity) is an emerging abnormal BC phenotype prevalent across the body weight, and hence BMI spectra. Sarcopenia and sarcopenic obesity in cancer are in most instances occult conditions, which have been independently associated with higher incidence of chemotherapy toxicity, shorter time to tumour progression, poorer outcomes of surgery, physical impairment and shorter survival. Although the mechanisms are yet to be fully understood, the associations with poorer clinical outcomes emphasise the value of nutritional assessment as well as the need to develop appropriate interventions to countermeasure abnormal BC. Sarcopenia and sarcopenic obesity create diverse nutritional requirements, highlighting the compelling need for a more comprehensive and differentiated understanding of energy and protein requirements in this heterogeneous population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".