Appetite loss/cachexia: basic science
Bibliographic record
Abstract
Involuntary weight loss is one of the hallmarks of advanced cancer. Healthy adults are generally highly resistant to attempts to lose body fat, which stores a remarkably constant amount of energy overall. Normally, highly precise controls work to ensure that energy expenditure and energy intake are matched (energy homeostasis), so that there is neither net loss nor net gain of body energy stores. In the patient with advanced cancer, however, a failure to maintain food intake relative to energy expenditure results in a failure to maintain energy homeostasis and is a primary contributor to involuntary weight loss. Reported levels of food intake in weight-losing patients with cancer are often lower than the basal metabolic rate of the same or similar patient populations. A full understanding of this weight loss is intimately linked to an understanding of the factors coordinating the balance between food intake and energy expenditure. Body weight is controlled by centers in the brain, notably the hypothalamus. Specific hypothalamic nuclei integrate cognitive, visual, taste, and olfactory sensory inputs, as well as peripheral signals indicating the status of physiological reserves of energy and protein in the whole body, the activity of the gastrointestinal tract, and nutrient intake. Three main elements of the appetite regulatory systems are considered here: the hypothalamic control of appetite, the reward pathway, and the sensory inputs that support food intake.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".