Bibliographic record
Abstract
PURPOSE OF REVIEW: Measurements of whole-body energy expenditure, body composition, and in-vivo metabolic fluxes are required to quantitatively understand involuntary weight loss in cancer cachexia. Such studies are rare because cancer cachexia occurs near the end of life when invasive metabolic tests may be precluded. Thus, models of cancer-associated weight loss are an important tool for helping to understand this debilitating condition. RECENT FINDINGS: A computational model of human macronutrient metabolism was recently developed that simulates the normal metabolic adaptations to semi-starvation and re-feeding. Here, this model was used to integrate data on the metabolic changes in patients with cancer cachexia. The resulting computer simulations show how the known metabolic disturbances synergize with reduced energy intake to result in a progressive loss of body weight, fat mass, and fat-free mass. The model was also used to simulate the effects of nutritional support and investigate inhibition of lipolysis versus proteolysis as potential therapeutic approaches for cancer cachexia. SUMMARY: Computational modeling is a new tool that can integrate clinical data on the metabolic changes in cancer cachexia and provide a conceptual framework to help understand involuntary weight loss and predict the effects of potential therapies.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".