Does the Subjective Global Assessment Predict Outcome in Critically Ill Medical Patients?
Bibliographic record
Abstract
BACKGROUND: The Subjective Global Assessment (SGA) is a validated nutrition assessment tool that is not commonly used to evaluate the nutritional status of patients admitted to the intensive care unit (ICU). OBJECTIVES: The aims of this study were to determine the prevalence of malnutrition in critically ill medical patients using the SGA and to determine whether the SGA was predictive of patient outcome. MATERIALS AND METHODS: A retrospective chart review was performed on 57 consecutive patients admitted to a single tertiary care medical ICU and requiring mechanical ventilation over a 6-month time period. All SGA assessments were performed by a single dietitian trained in this assessment technique. Multiple factors including patient demographics, severity of illness, length of mechanical ventilation, length of ICU stay, and mortality were abstracted from the charts. RESULTS: The prevalence of malnutrition on admission as assessed by the SGA was 35%. Severity of illness as determined by Acute Physiology and Chronic Health Evaluation II (APACHE II) score was not different between the SGA groups. Mortality rates were significantly higher in the moderately (45.5%) and severely malnourished (55.6%) groups than in the well-nourished group (10.8%; P = .004). CONCLUSION: Malnutrition on admission is common in critically ill medical patients. Malnutrition, as assessed by SGA at admission to ICU, is associated with increased mortality and thus can serve as a valuable prognostic tool in the assessment of critically ill patients. Given that that the SGA is a simple bedside assessment, it should be considered for routine use in assessing critically ill patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".