Improving Malnutrition Screening among Hemodialysis Patients
Bibliographic record
Abstract
Individuals receiving hemodialysis are at increased risk of malnutrition; however, regular diagnosis of malnutrition using subjective global assessment (SGA) is time-consuming. This study aimed to determine whether the Canadian Nutrition Screening Tool (CNST) or the Geriatric Nutrition Risk Index (GNRI) screening tools could accurately identify hemodialysis patients at risk for malnutrition. A retrospective medical chart review was conducted for in-centre day shift hemodialysis patients (n = 95) to obtain the results of the SGA assessment and the CNST screener and to calculate the GNRI score. Sensitivity and specificity analyses showed only a fair agreement between the SGA and CNST (sensitivity = 20%; specificity 96%; κ = .210 (95% CI, -0.015 to .435), p < .05) and between the SGA and GNRI (sensitivity = 35%; specificity = 88%; κ = .248 (95% CI, .017 to .479), p < .05). There was no significant statistical difference between the accuracy of either tool in identifying patients at risk of malnutrition (p = .50). The CNST and GNRI do not accurately screen for risk of malnutrition in the hemodialysis population; therefore, further studies are needed to determine an effective malnutrition screening tool in this population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".