Guidance for assessment of the muscle mass phenotypic criterion for the Global Leadership Initiative on Malnutrition diagnosis of malnutrition
Bibliographic record
Abstract
The Global Leadership Initiative on Malnutrition (GLIM) provides consensus criteria for the diagnosis of malnutrition that can be widely applied. The GLIM approach is based on the assessment of three phenotypic (weight loss, low body mass index, and low skeletal muscle mass) and two etiologic (low food intake and presence of disease with systemic inflammation) criteria, with diagnosis confirmed by any combination of one phenotypic and one etiologic criterion fulfilled. Assessment of muscle mass is less commonly performed than other phenotypic malnutrition criteria, and its interpretation may be less straightforward, particularly in settings that lack access to skilled clinical nutrition practitioners and/or to body composition methodologies. In order to promote the widespread assessment of skeletal muscle mass as an integral part of the GLIM diagnosis of malnutrition, the GLIM consortium appointed a working group to provide consensus-based guidance on assessment of skeletal muscle mass. When such methods and skills are available, quantitative assessment of muscle mass should be measured or estimated using dual-energy x-ray absorptiometry, computerized tomography, or bioelectrical impedance analysis. For settings where these resources are not available, then the use of anthropometric measures and physical examination are also endorsed. Validated ethnic- and sex-specific cutoff values for each measurement and tool are recommended when available. Measurement of skeletal muscle function is not advised as surrogate measurement of muscle mass. However, once malnutrition is diagnosed, skeletal muscle function should be investigated as a relevant component of sarcopenia and for complete nutrition assessment of persons with malnutrition.
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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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".