Adult Starvation and Disease‐Related Malnutrition
Bibliographic record
Abstract
BACKGROUND & AIMS: Multiple definitions for malnutrition syndromes are found in the literature resulting in confusion. Recent evidence suggests that varying degrees of acute or chronic inflammation are key contributing factors in the pathophysiology of malnutrition that is associated with disease or injury. METHODS: An International Guideline Committee was constituted to develop a consensus approach to defining malnutrition syndromes for adults in the clinical setting. Consensus was achieved through a series of meetings held at the A.S.P.E.N. and ESPEN Congresses. RESULTS: It was agreed that an etiology-based approach that incorporates a current understanding of inflammatory response would be most appropriate. The Committee proposes the following nomenclature for nutrition diagnosis in adults in the clinical practice setting. "Starvation-related malnutrition", when there is chronic starvation without inflammation, "chronic disease-related malnutrition", when inflammation is chronic and of mild to moderate degree, and "acute disease or injury-related malnutrition", when inflammation is acute and of severe degree. CONCLUSIONS: This commentary is intended to present a simple etiology-based construct for the diagnosis of adult malnutrition in the clinical setting. Development of associated laboratory, functional, food intake, and body weight criteria and their application to routine clinical practice will require validation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".