Sarcopenia and Cachexia in Older Patients with Cancer: Pathophysiology, Diagnosis, Impact on Outcomes, and Management Strategies
Bibliographic record
Abstract
Sarcopenia and cachexia are two common and overlapping but distinct muscle wasting syndromes that predict adverse outcomes and undermine quality of life among older adults with cancer. Despite their prognostic value and negative effects on older patients' well-being, sarcopenia and cachexia are not routinely or adequately assessed and managed in clinical oncology practice. However, efforts to recognize and manage sarcopenia and cachexia at diagnosis and during follow-up may have beneficial effects on muscle mass, physical function, and quality of life among older adults with cancer, although evidence on long-term clinical outcomes in response to targeted interventions has yet to be established. This comprehensive review attempts to (i) delineate the differences in the pathophysiology and clinical manifestations between sarcopenia and cachexia, (ii) clarify how sarcopenia and cachexia are defined in the geriatric oncology literature, (iii) describe methods for assessing sarcopenia and cachexia in clinical practice, (iv) review the prognostic value of sarcopenia and cachexia among older patients, particularly those undergoing systemic cancer treatment, and (v) discuss evidence-based strategies aimed at managing sarcopenia and cachexia for older adults with cancer.
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.
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) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".