Prospects for protein-energy formulas in patients with osteoporosis
Bibliographic record
Abstract
Osteoporosis is a multifactorial metabolic bone disease characterized by reduced bone mineral density and compromised bone microarchitecture, leading to skeletal fractures from minimal external trauma. Osteoporosis is frequently accompanied by sarcopenia, reflecting the ongoing interaction between muscle and bone tissues from embryogenesis through advanced age, manifested at biochemical, endocrine, and mechanical levels, which may exacerbate both conditions. One significant risk factor for patients with osteosarcopenia is malnutrition, associated with increased mortality, disability, cognitive decline, and a higher incidence of falls and fractures. Nutritional support and physical activity represent promising approaches for managing these patients. Timely administration of supplemental protein-energy formulas may offer additional benefits for patients with osteosarcopenia; however, currently, no standardized nutritional support strategy exists due to population heterogeneity among patients with osteoporosis and a lack of conclusive data on the benefits of such interventions without diagnosed sarcopenia. This review summarizes contemporary data on nutrition in osteoporosis, evaluates existing nutritional support strategies, and assesses their potential efficacy. Special attention is given to the role of proteins, vitamins, and micronutrients in the prevention and treatment of osteoporosis, with recommended daily doses of these nutrients presented. The information provided herein may serve as a foundation for future research and the development of optimal nutritional support strategies for this population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".