A lifespan approach to osteoarthritis prevention
Bibliographic record
Abstract
Prevention is an attractive solution for the staggering and increasingly unmanageable burden of osteoarthritis. Despite this, the field of osteoarthritis prevention is relatively immature. To date, most of what is known about preventing osteoarthritis and risk factors for osteoarthritis is relative to the disease (underlying biology and pathophysiology) of osteoarthritis, with few studies considering risk factors for osteoarthritis illness, the force driving the personal, financial and societal burden. In this narrative review we will discuss what is known about osteoarthritis prevention, propose actionable prevention strategies related to obesity and joint injury which have emerged as important modifiable risk factors, identify where evidence is lacking, and give insight into what might be possible in terms of prevention by focussing on a lifespan approach to the illness of osteoarthritis, as opposed to a structural disease of the elderly. By targeting a non-specialist audience including scientists, clinicians, students, industry employees and others that are interested in osteoarthritis but who do not necessarily focus on osteoarthritis, the goal is to generate discourse and motivate inquiry which propel the field of osteoarthritis prevention into the mainstream.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".