Cancer cachexia is defined by an ongoing loss of skeletal muscle mass
Bibliographic record
Abstract
Since 2007, a quantitative, specific and precise approach to the detection of muscle loss has become accessible with the advent of image-based assessments. Computed tomography images acquired as part of standard cancer care are the serendipitous substrate for these analyses. Three radiologically-determined abnormalities, sarcopenia (severe muscle depletion), catabolic loss of muscle over time, and reduced muscle radiation attenuation associate with progressive functional impairment, treatment-related complications, reduced quality of life, and mortality. Fundamental understanding of muscle wasting in cancer cachexia has been developed on a base of clinical and experimental studies, which have identified alterations in muscle protein synthesis, autophagy and ubiquitin-mediated proteolysis as key contributors to muscle loss. The etiology of cancer-associated muscle wasting is multifactorial. Tumor metabolism captures energy fuels and amino acids, and a suite of tumor-derived molecules elicits catabolic pathways at the tissue level in muscle. Endocrine, neural and inflammatory derangements add further catabolic drive. Antineoplastic agents make a substantial contribution to muscle wasting by directly action on muscle cells, as well as secondarily via their systemic side effects. Encouraging data is emerging as to the potential reversibility of muscle loss and/or reduced muscle radiation attenuation through modulation of specific mechanisms. In the first line, pain and symptom management is a key element of the prevention of catabolic loss of muscle. Intake of intake of high-quality proteins and ω-3 polyunsaturated fatty acids support retention or gain of muscle mass. While there is no approved drug therapy for the indication of cancer-associated muscle wasting, there is preliminary evidence for robust gain of skeletal muscle mass in research studies of new therapeutics including inhibitors of mitogen-activated protein kinase kinases and ghrelin receptor agonists.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".