Sarcopenic diabetes is an under-recognized and unmet clinical priority. A call for action from the European Society for Clinical Nutrition and Metabolism and the Diabetes Nutrition Study Group
Bibliographic record
Abstract
Diabetes mellitus is a systemic chronic disease with growing prevalence and potential multiorgan complications leading to clinical, social, and economic burdens. Nutritional and metabolic derangements are important components of both type 1 (T1DM) and type 2 diabetes (T2DM), but assessment of nutritional state, body composition and muscle function is commonly neglected. Likely reasons include high prevalence of overweight, obesity, or excess visceral fat in highly-prevalent T2DM, potentially diverting attention from undernutrition risk. Diabetes and adiposity are mechanistically related to sarcopenia, defined as reduction of skeletal muscle strength and mass, through complex muscle-catabolic derangements, conferring additional risk for negative outcomes. Awareness of diabetes-induced muscle abnormalities remains low among healthcare professionals, patients and policymakers, contributing to research, knowledge and practice gaps. Lifestyle recommendations and treatments centered on nutritional care and physical activity to preserve and improve muscle mass and function remain poorly implemented. The European Society for Clinical Nutrition and Metabolism (ESPEN) and the Diabetes Nutrition Study Group (DNSG), reference group for the European Association for the Study of Diabetes, recognize sarcopenic diabetes as a distinct clinical condition and priority for research and education, and call for action to enhance awareness, stimulate research and promote consensus on sarcopenic diabetes diagnostic criteria, prevention and management.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".