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 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 | 0.013 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".