Cancer cachexia: rationale for the MENAC (Multimodal—Exercise, Nutrition and Anti-inflammatory medication for Cachexia) trial
Bibliographic record
Abstract
Cancer cachexia is a multifactorial syndrome characterised by an ongoing loss of skeletal muscle mass that cannot be fully reversed by conventional nutritional support alone. Cachexia has a high prevalence in cancer and a major impact on patient physical function, morbidity and mortality. Despite the consequences of cachexia, there is no licensed treatment for cachexia and no accepted standard of care. It has been argued that the multifactorial genesis of cachexia lends itself to therapeutic targeting through a multimodal treatment. Following a successful phase II trial, a phase III randomised controlled trial of a multimodal cachexia intervention is under way. Termed the MENAC trial (Multimodal-Exercise, Nutrition and Anti-inflammatory medication for Cachexia), this intervention is based on evidence to date and consists of non-steroidal anti-inflammatory drugs and eicosapentaenoic acid to reduce inflammation, a physical exercise programme using resistance and aerobic training to increase anabolism, as well as dietary counselling and oral nutritional supplements to promote energy and protein balance. Herein we describe the development of this trial. TRIAL REGISTRATION NUMBER: NCT02330926.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".