Using technology to assess nutritional status and optimize nutrition therapy in critically ill patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Malnutrition is prevalent in critically ill patients and is linked to worse outcomes such as prolonged mechanical ventilation, length of intensive care unit (ICU) stay, and increased mortality. Therefore, nutritional therapy is important. However, it is often difficult to accurately identify those at high malnutrition risk and to optimize nutritional support. Different technological modalities have therefore been developed to identify patients at high nutritional risk and to guide nutritional support in an attempt to optimize outcomes. RECENT FINDINGS: Computed tomography (CT), ultrasound (US), and bioelectrical impedance analysis are tools that allow assessment of lean body mass and detection of sarcopenia, which is a significant marker of poor nutrition. The use of indirect calorimetry allows the determination of resting energy expenditure to serve as a guide to providing optimal nutrition intake in ICU patients. SUMMARY: By using CT, US, or bioelectrical impedance analysis, detection of sarcopenia can be undertaken in patients admitted to the ICU. This allows for an accurate picture of underlying nutritional status to help clinicians focus on nutritional support for these patients. Subsequently, indirect calorimetry can be used to guide optimal nutrition therapy and caloric intake in critically ill patients. However, whether these methods result in improved outcomes in critically ill patients remains to be validated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".