Initial development and validation of a novel nutrition risk, sarcopenia, and frailty assessment tool in mechanically ventilated critically ill patients: The NUTRIC‐SF score
Bibliographic record
Abstract
BACKGROUND: Nutrition risk, sarcopenia, and frailty are interrelated. They may be due to suboptimal or prevented by optimal nutrition intake. The combination of nutrition risk (modified nutrition risk in the critically ill [mNUTRIC]), sarcopenia (SARC-F combined with calf circumference [SARC-CALF]), and frailty (clinical frailty scale [CFS]) in a single score may better predict adverse outcomes and prioritize resources for optimal nutrition in the intensive care unit (ICU) METHODS: This is a retrospective analysis of a single-center prospective observational study that enrolled mechanically ventilated adults with expected ≥96 h of ICU stay. SARC-F and CFS questionnaires were administered to patient's next-of-kin and mNUTRIC were calculated. Right calf circumference was measured. Nutrition data were collected from nursing record. The high-risk scores (mNUTRIC ≥ 5, SARC-CALF > 10, or CFS ≥ 4) of these variables were combined to become the nutrition risk, sarcopenia, and frailty (NUTRIC-SF) score (range: 0-3). RESULTS: Eighty-eight patients were analyzed. Increasing mNUTRIC was independently associated with 60-day mortality, whereas increasing SARC-CALF and CFS showed a strong trend towards a higher 60-day mortality. Discriminative ability of NUTRIC-SF for 60-day mortality is better than its component (C-statistics, 0.722; 95% confidence interval [CI], 0.677-0.868). Every increment of 300 kcal/day and 30 g/day is associated with a trend towards higher rate of discharge alive for high (≥2; adjusted hazard ratio, 1.453 [95% CI, 0.991-2.130] for energy; 1.503 [0.936-2.413] for protein) but not low (<2) NUTRIC-SF score. CONCLUSION: NUTRIC-SF may be a clinically relevant risk stratification tool in the ICU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".