Cancer-associated malnutrition, cachexia and sarcopenia: the skeleton in the hospital closet 40 years later
Bibliographic record
Abstract
An awareness of the importance of nutritional status in hospital settings began more than 40 years ago. Much has been learned since and has altered care. For the past 40 years several large studies have shown that cancer patients are amongst the most malnourished of all patient groups. Recently, the use of gold-standard methods of body composition assessment, including computed tomography, has facilitated the understanding of the true prevalence of cancer cachexia (CC). CC remains a devastating syndrome affecting 50-80 % of cancer patients and it is responsible for the death of at least 20 %. The aetiology is multifactorial and complex; driven by pro-inflammatory cytokines and specific tumour-derived factors, which initiate an energy-intensive acute phase protein response and drive the loss of skeletal muscle even in the presence of adequate food intake and insulin. The most clinically relevant phenotypic feature of CC is muscle loss (sarcopenia), as this relates to asthenia, fatigue, impaired physical function, reduced tolerance to treatments, impaired quality of life and reduced survival. Sarcopenia is present in 20-70 % depending on the tumour type. There is mounting evidence that sarcopenia increases the risk of toxicity to many chemotherapy drugs. However, identification of patients with muscle loss has become increasingly difficult as 40-60 % of cancer patients are overweight or obese, even in the setting of metastatic disease. Further challenges exist in trying to reverse CC and sarcopenia. Future clinical trials investigating dose reductions in sarcopenic patients and dose-escalating studies based on pre-treatment body composition assessment have the potential to alter cancer treatment paradigms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".