2025 Updates to the Malnutrition Care Score: Expanding Age Criteria to Enhance Malnutrition Care and Improve Health Equity
Bibliographic record
Abstract
Credentialed nutrition and dietetics practitioners are uniquely positioned to communicate annual changes to the Global Malnutrition Composite Score to promote adoption of this landmark electronic clinical quality measure. With input from various stakeholders, advances in nutrition care processes, and Centers for Medicare and Medicaid Services guidance, the Academy of Nutrition and Dietetics and the Commission on Dietetic Registration have instituted improvements to better align with the measure's intent. Beginning in reporting year 2026, the Global Malnutrition Composite Score will undergo a name change to the Malnutrition Care Score, consistent with its goal to enhance the identification and treatment of malnutrition in patients hospitalized for 24 hours or longer. In addition, a significant expansion of the inclusion group occurs from 65 years and older to 18 years and older, recognizing the presence of malnutrition in younger people. Excluded for the first time are hospice patients, whose care needs are likely inconsistent with the measure's intent. Refinements in measure logic will ease the implementer's burden and, more importantly, better align with clinical best practices by prioritizing the most recent nutrition assessment to identify patients with worsening clinical courses or iatrogenic malnutrition during their hospital stay and more accurately capture eligible encounters based on admission and discharge dates. Value set changes ensure accurate and automated collection of information to meet the updates. Armed with this knowledge, credentialed nutrition and dietetics practitioners can communicate Malnutrition Care Score improvements and offer invaluable expertise to assist organizations in the measure's implementation and demonstrating the effectiveness of malnutrition care.
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".