Bibliographic record
Abstract
Abstract In older persons, the combination of osteopenia/osteoporosis and sarcopenia - known as osteosarcopenia - has been proposed as a subset of individuals at higher risk of adverse outcomes. The pathophysiology of osteosarcopenia results from a complex set of interactions between bone, muscle, and fat influenced by genetic, mechanical, and biochemical factors. There is a complex crosstalk system between muscle and bone, in which fat has become a new player. This communication system comprises osteokines, myokines, adipokines and other secreted factors (i.e., extracellular vesicles, exosomes and microRNAs), some of which have positive or negative effects on muscle and/or bone metabolism and function. These effects include alterations in most of the hallmarks of aging, triggered by conditions such as sedentarism, obesity, malnutrition, inflammation, and menopause, which induce tissue loss and dysfunction. In contrast, anabolic factors could be stimulated to prevent muscle and bone loss of mass and function. Therefore, osteosarcopenia is an optimal condition in which geroscience-based approaches could target several hallmarks of aging to have a beneficial dual effect on bone and muscle. This session will discuss the biological characteristics of osteosarcopenia shared by muscle and bone, their relevance in the pathogenesis of the condition, and their potential use as therapeutic targets in osteosarcopenia.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".