Clinical considerations on nutrition and lifestyle in old people with osteoporosis and sarcopenia by the CReI-Food study group of the Italian College of Rheumatologists
Bibliographic record
Abstract
Osteoporosis is considered the most frequent rheumatic pathology in the elderly population. It is often accompanied by sarcopenia, defined as the progressive loss of skeletal muscle mass and strength. Together, these conditions increase the risk of falls, fractures, disability, lower quality of life, and frequent hospitalization. This means increased healthcare costs for all European countries, due to the increase in the average age and elderly population. Multiple pharmacological therapies are available for osteoporosis, but the prevention seems to be an important tool to reduce the risk of fracture and hospitalization of patients. A healthy lifestyle, consisting of the right amount of nutrients and micronutrients and physical exercise can help prevent the development of osteoporosis and sarcopenia in the elderly. This review brings together the information present in the literature on the benefits that nutrients and micronutrients, naturally present in foods, have on the prevention of these pathologies. it is suggested that a correct intake of proteins, calcium, phosphorus, magnesium, vitamin.D, vitamin C and antioxidants, vitamin K and omega 3, associated with constant physical exercise, can help to contain osteoporosis. It also indicates the right physical exercise for older osteoporotic adults. This document drawn up by the CReI-Food study group of the Italian College of Rheumatologists aims to provide, based on the most recent scientific evidence, indications on correct nutrition and lifestyle for the prevention and treatment of osteoporosis in older people.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".