The prognostic value of modified NUTRIC score for patients in cardiothoracic surgery recovery unit: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Malnutrition is highly prevalent in critically ill patients. The modified Nutrition Risk in the Critically ill (mNUTRIC) score has been introduced to evaluate the nutritional risk of patients in an intensive care unit (ICU). The mNUTRIC score is a predictive factor of mortality for patients in a medical or mixed ICU, whereas the relationship between mNUTRIC and prognosis of patients in a cardiothoracic surgery recovery unit (CSRU) is unclear and related researches are limited. METHODS: We conducted this retrospective cohort study to explore the value of mNUTRIC score in CSRU patients. We identified totally 4059 patients from the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC III) database. RESULTS: The optimal cut-off value of mNUTRIC score was 4 and a total of 1498 (36.9%) patients were considered to be at high nutritional risk (mNUTRIC ≥ 4). A multivariate logistic regression model indicated that patients at high nutritional risk have higher hospital mortality compared to those at low nutritional risk (odds ratio = 2.49, 95% confidence interval (CI) = 1.32-4.70, p = 0.005]. Furthermore, a Cox regression model was established adjusted for age, white blood cell and body mass index. The Kaplan-Meier curve indicated that patients at high nutritional risk have poorer 365-days [hazard ratio (HR) = 1.76, 95% CI = 1.30-2.37, p < 0.001] and 1000-days (HR = 2.30, 95% CI = 1.87-2.83, p < 0.001) overall survival. CONCLUSIONS: The mNUTRIC score could not only predict hospital mortality, but also be an independent prognostic factor for long-term survival in CSRU patients. More well-designed clinical trials are needed to verify and update our findings.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".