Identifying critically ill patients who benefit the most from nutrition therapy: the development and initial validation of a novel risk assessment tool
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: To develop a scoring method for quantifying nutrition risk in the intensive care unit (ICU). METHODS: A prospective, observational study of patients expected to stay > 24 hours. We collected data for key variables considered for inclusion in the score which included: age, baseline APACHE II, baseline SOFA score, number of comorbidities, days from hospital admission to ICU admission, Body Mass Index (BMI) < 20, estimated % oral intake in the week prior, weight loss in the last 3 months and serum interleukin-6 (IL-6), procalcitonin (PCT), and C-reactive protein (CRP) levels. Approximate quintiles of each variable were assigned points based on the strength of their association with 28 day mortality. RESULTS: A total of 597 patients were enrolled in this study. Based on the statistical significance in the multivariable model, the final score used all candidate variables except BMI, CRP, PCT, estimated percentage oral intake and weight loss. As the score increased, so did mortality rate and duration of mechanical ventilation. Logistic regression demonstrated that nutritional adequacy modifies the association between the score and 28 day mortality (p = 0.01). CONCLUSIONS: This scoring algorithm may be helpful in identifying critically ill patients most likely to benefit from aggressive nutrition therapy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it