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Record W2930675045 · doi:10.1002/jcsm.12383

GLIM criteria for the diagnosis of malnutrition – A consensus report from the global clinical nutrition community

2019· article· en· W2930675045 on OpenAlexaff
Tommy Cederholm, Gordon L. Jensen, María Isabel Toulson Davisson Correia, Marı́a Cristina González, Ryoji Fukushima, T. Higashiguchi, Gertrudis Adrianza de Baptista, Rocco Barazzoni, Renée Blaauw, Andrew J.S. Coats, Adriana Crivelli, David C. Evans, Leah Gramlich, Vanessa Fuchs‐Tarlovsky, Heather Keller, Luisito O. Llido, Ainsley Malone, Kris M. Mogensen, John E. Morley, Maurizio Muscaritoli, Ibolya Nyulasi, Matthias Pirlich, Veeradej Pisprasert, M.A.E. de van der Schueren, S. Siltharm, Pierre Singer, Kelly A. Tappenden, N Velasco, Dan Linetzky Waitzberg, Preyanuj Yamwong, Jianchun Yu, A. Van Gossum, Charlene Compher

Bibliographic record

VenueJournal of Cachexia Sarcopenia and Muscle · 2019
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsResearch Institute for AgingUniversity of WaterlooUniversity of Alberta
Fundersnot available
KeywordsMalnutritionGrading (engineering)MedicineConsensus conferenceGlobal healthIntensive care medicinePediatricsPublic healthNursingPathologyInternal medicine

Abstract

fetched live from OpenAlex

RATIONALE: This initiative is focused on building a global consensus around core diagnostic criteria for malnutrition in adults in clinical settings. METHODS: In January 2016, the Global Leadership Initiative on Malnutrition (GLIM) was convened by several of the major global clinical nutrition societies. GLIM appointed a core leadership committee and a supporting working group with representatives bringing additional global diversity and expertise. Empirical consensus was reached through a series of face-to-face meetings, telephone conferences, and e-mail communications. RESULTS: A two-step approach for the malnutrition diagnosis was selected, i.e., first screening to identify "at risk" status by the use of any validated screening tool, and second, assessment for diagnosis and grading the severity of malnutrition. The malnutrition criteria for consideration were retrieved from existing approaches for screening and assessment. Potential criteria were subjected to a ballot among the GLIM core and supporting working group members. The top five ranked criteria included three phenotypic criteria (weight loss, low body mass index, and reduced muscle mass) and two etiologic criteria (reduced food intake or assimilation, and inflammation or disease burden). To diagnose malnutrition at least one phenotypic criterion and one etiologic criterion should be present. Phenotypic metrics for grading severity as Stage 1 (moderate) and Stage 2 (severe) malnutrition are proposed. It is recommended that the etiologic criteria be used to guide intervention and anticipated outcomes. The recommended approach supports classification of malnutrition into four etiology-related diagnosis categories. CONCLUSION: A consensus scheme for diagnosing malnutrition in adults in clinical settings on a global scale is proposed. Next steps are to secure further collaboration and endorsements from leading nutrition professional societies, to identify overlaps with syndromes like cachexia and sarcopenia, and to promote dissemination, validation studies, and feedback. The diagnostic construct should be re-considered every 3-5 years.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.045
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0140.006
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0070.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.003

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.

Opus teacher head0.132
GPT teacher head0.440
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1,652
Published2019
Admission routes1
Has abstractyes

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