Frequency of and Reasons for False-Positive Consults Generated by the Malnutrition Screening Tool
Bibliographic record
Abstract
BACKGROUND: Nutrition screening on admission is one way to identify patients with malnutrition. The Malnutrition Screening Tool (MST) is a commonly used screening tool but has been found to generate false-positive consults. PURPOSE: The purpose of this research was two-fold: (1) to determine the percentage of nursing screens, using the MST, that generated a false-positive consult for a registered dietitian, and (2) to identify the reasons for these false-positive consults. METHODS: During a 3-month period, registered dietitians documented the number of false-positive consults received from the MST and reasons they were received. RESULTS: Of the registered dietitian consults generated, 5.5% were deemed false-positive. The most common reason for a false-positive consult was patient-reported weight loss that had resolved. CONCLUSIONS: As nurses are integral to completion of the MST, data generated can be used in ongoing education of nursing staff.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".