How much does reduced food intake contribute to cancer-associated weight loss?
Bibliographic record
Abstract
PURPOSE OF REVIEW: An international consensus group defined cancer cachexia as a syndrome of involuntary weight loss, characterized by loss of skeletal muscle (with or without fat loss), which is driven by a variable combination of reduced food intake and altered metabolism.This review presents recent studies that evaluated the contribution of reduced food intake to cancer-associated weight loss. RECENT FINDINGS: Four studies examined food intake in relation to weight loss. Heterogeneity among studies rendered aggregation and interpretation of results challenging. Despite these limitations, reduced food intake had consistent significant, independent associations with weight loss. However, reduced food intake did not explain all the variation in weight loss; and limited data suggests factors related to alterations in metabolism (e.g. increased resting energy expenditure, systemic inflammation) are also contributing to weight loss. SUMMARY: Reduced food intake is a significant contributor to cancer-associated weight loss. Understanding the magnitude of the association between food intake and weight loss may improve when it is possible to account for alterations in metabolism. Efforts to align clinical assessments of food intake to reduce heterogeneity are needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".