Evaluation of Nutrition Status Using the Subjective Global Assessment: Malnutrition, Cachexia, and Sarcopenia
Bibliographic record
Abstract
The subjective global assessment (SGA) is a nutrition assessment tool that refers to an overall evaluation of a patient's history and physical examination and uses structured clinical parameters to diagnose malnutrition. The SGA is known to be a reliable and valid tool that predicts morbidity and mortality associated with malnutrition. The objective of SGA is to identify patients likely to benefit from nutrition intervention and therefore to identify persons in whom inadequate nutrition intake or absorption explain features of malnutrition, including body wasting. There are other conditions that cause weight loss, muscle wasting, and fat loss, including cachexia and sarcopenia. Acknowledging that these 2 last conditions differ in their mechanism of body wasting and consequently in the outcomes of nutrition intervention, the practitioner needs a tool to identify when malnutrition is the dominating factor to explain body wasting. The SGA form has been revised to clearly reflect the key concepts behind the diagnosis of malnutrition and help to distinguish this condition from other wasting conditions. This review presents the revised SGA form and guidance document. Using case studies, it illustrates the 3 wasting conditions, their overlap, and how the SGA identifies malnutrition as a dominating factor of body wasting and thus individuals who require nutrition intervention.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".