Clinical measurement properties of malnutrition assessment tools for use with patients in hospitals: a systematic review
Bibliographic record
Abstract
BACKGROUND: The use of malnutrition outcome measures (OM) by registered dietitians (RD) with inpatients in hospitals has increased promoting the achievement of nutritional care goals and supporting decision-making for the allocation of nutritional care resources in hospitals. There are 3 commonly used OMs: Subjective Global Assessment (SGA), Patient Generated-Subjective Global Assessment (PG-SGA) and Mini Nutritional Assessment (MNA). The purpose of this current study was to systematically review the evidence of the clinical measurement properties of malnutrition assessment tools for use with patients admitted in hospitals. METHODS: MEDLINE, Cinahl, EMBASE, and PubMed were searched for articles published between 2000 and 2019. Research articles were selected if they established reliability, validity, and responsiveness to change properties of the SGA, PG-SGA and MNA tools, were written in English, and used any of these OMs as an outcome measure. Abstracts were not considered. The risk of bias within studies was assessed using the Quality Appraisal for Clinical Measurement Study (QA-CMS). RESULTS: Five hundred five studies were identified, of which 34 articles were included in the final review: SGA (n = 8), PG-SGA (n = 13), and MNA (n = 13). Of the 34 studies, 8 had a quality score greater than 75%; 23 had a quality score of 40-75% and 3 studies had a quality score of less than 40%. PG-SGA was found to have excellentdiagnostic accuracy (ROC: 0.92-0.975; Sensitivity: 88.6-98%; Specificity: 82-100%), sufficient internal consistency (Cronbach's alpha: 0.722-0.73), and strong test-retest reliability (r = 0.866). There was insufficient evidence to suggest adequate diagnostic accuracy and good inter-rater reliability for SGA. Only one study examined the minimum detectable change of MNA (MDC = 2.1). CONCLUSIONS: The evidence of validity for the existing malnutrition assessment tools supports the use of these tools, but more studies with sound methodological quality are needed to assess the responsiveness of these OMs to detect the change in nutritional status.
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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 | 0.030 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".