Prevalence of nutritional risk and malnutrition in hospitalized patients: a retrospective, cross-sectional study of single-day screening
Bibliographic record
Abstract
Hospital malnutrition remains a significant public health issue, particularly in developing countries. The Global Leadership Initiative on Malnutrition (GLIM) proposed homogenizing criteria to standardize malnutrition diagnosis. This study aimed to retrospectively determine the prevalence of nutritional risk and malnutrition diagnoses among hospitalized patients using the Nutritional Risk Screening (NRS)-2002 screening instrument and the GLIM criteria, respectively. We conducted a retrospective, cross-sectional study from nutritional records of patients hospitalized in a single centre 2021. Nutrition data from records included medical diagnosis, gender, length of stay, age, weight, height, body mass index, weight loss, calf circumference, and middle upper arm circumference. Nutritional risk and malnutrition were evaluated using NRS-2002 and GLIM criteria. Its concordance was further evaluated by using a Kappa test. The study included 616 records of patients; 52.3% ( n = 322) of the population were male. The prevalence of nutritional risk, according to NRS-2002, was 69.5% ( n = 428). Nutritional risk as well as malnutrition diagnosis according to GLIM criteria was observed in 87.8% ( n = 374) of patienttritional risk and malnutrition were evaluated using NRS-2002 and GLIM criteria. Its concordance was further evaluated by using a Kappa test. Ws. Tools showed a strong concordance (κ= 0.732). All anthropometric data, except for height, were found to be significantly different between patients with moderate and severe malnutrition ( p < 0.05). Our findings highlight a high prevalence of malnutrition in this group of hospitalized patients in Mexico. NRS-2002 demonstrated good agreement with the diagnosis of malnutrition according to GLIM criteria and could be considered part of the straightforward two-step approach for malnutrition; however, further studies are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".