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Record W6976860316 · doi:10.60692/2hw9d-9kv94

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

2019· article· en· W6976860316 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic Research in Diverse Fields
Canadian institutionsResearch Institute for AgingUniversity of WaterlooUniversity of Alberta
Fundersnot available
KeywordsMalnutritionGrading (engineering)Global healthConsensus conferenceMEDLINEDiseaseIntervention (counseling)

Abstract

fetched live from OpenAlex

Summary 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.213
GPT teacher head0.404
Teacher spread0.191 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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