Bibliographic record
Abstract
Osteoarthritis, the most common joint disease experienced by many older adults and one that is increasing as societies age remains largely incurable and highly disabling. As such, and in light of the failure of most palliative pharmacologic strategies to date to reverse or mitigate this painful condition, it appears all avenues of potential progress added to those already being explored may yet prove fruitful. Based on emerging and quite promising data outlining a possible link between aspects of osteoarthritis joint damage and muscle deficits, this mini-review elected to examine the most current data in regard to discern whether: 1) Muscle dysfunction continues to be an important potentially modifiable health determinant whose presence might influence the onset and progression of osteoarthritis through direct as well as indirect pathways, and 2) any evidence pointing to its possible detection, categorization, and remediation as being desirable in this regard if indicated. Based on the bulk of available data housed in PUBMED, GOOGLE SCHOLAR, Science Direct, and PubMed Central as of August 15, 2023, it is concluded that a causative, mediating or moderating role for muscle dysfunction in the realm of osteoarthritis cannot be ignored as per data related to its most common form, knee osteoarthritis. Future efforts to advance this line of inquiry that proceed from a multi component muscle specific and whole joint perspective of osteoarthritis disability may prove of immense personal and societal impact and benefit.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".