Exercise training as treatment in cancer cachexia
Bibliographic record
Abstract
Cachexia is a wasting syndrome that may accompany a plethora of diseases, including cancer, chronic obstructive pulmonary disease, aids, and rheumatoid arthritis. It is associated with central and systemic increases of pro-inflammatory factors, and with decreased quality of life, response to pharmacological treatment, and survival. At the moment, there is no single therapy able to reverse cachexia many symptoms, which include disruption of intermediary metabolism, endocrine dysfunction, compromised hypothalamic appetite control, and impaired immune function, among other. Growing evidence, nevertheless, shows that chronic exercise, employed as a tool to counteract systemic inflammation, may represent a low-cost, safe alternative for the prevention/attenuation of cancer cachexia. Despite the well-documented capacity of chronic exercise to counteract sustained disease-related inflammation, few studies address the effect of exercise training in cancer cachexia. The aim of the present review was hence to discuss the results of cachexia treatment with endurance training. As opposed to resistance exercise, endurance exercise may be performed devoid of equipment, is well tolerated by patients, and an anti-inflammatory effect may be observed even at low-intensity. The decrease in inflammatory status induced by endurance protocols is paralleled by recovery of various metabolic pathways. The mechanisms underlying the response to the treatment are considered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".