Are Dietitians Documenting Malnutrition Based on Subjective Global Assessment Category?
Bibliographic record
Abstract
Purpose: This study reports on dietitian use of the Nutrition Care Process Terminology (NCPT) diagnosis of malnutrition based on Subjective Global Assessment (SGA). Methods: Nutrition assessment reports for adults in medical, surgical, and cardiac units in 13 Canadian hospitals were retrospectively examined for a 6-week period in 2014. Reports with a SGA and NCPT diagnosis were included regardless of why the patient was seen by the dietitian. Results: Of the 932 nutrition assessment reports, 857 (92%) included an SGA. Based on SGA, the prevalence of mild to moderate malnutrition (SGA B) and severe malnutrition (SGA C) was 53.4% (n = 458) and 10.0% (n = 86), respectively. When categorized as severely malnourished, the most common NCPT diagnoses were “malnutrition” (n = 55, 72.4%), “inadequate oral intake” (n = 11, 14.5%), and “inadequate protein-energy intake” (n = 10,13.1%). Among those with SGA B and C, the assignment of the NCPT malnutrition diagnosis was 19.8% (n = 95). Conclusions: Dietitians play a key role in the prevention, identification, and treatment of malnutrition in the hospitalized patient and are well positioned to take a leadership role in improving its documentation. Ongoing audits, staff support, and training regarding NCPT use may improve the application of the malnutrition diagnosis. Future research examining dietitian barriers to using the malnutrition diagnosis would be valuable.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".