Towards secondary prevention of early knee osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) of the knee is the most common arthritic disease, yet a convincing drug treatment is not available. The current narrative review focuses on integration of scientific evidence and professional experience to illustrate which management approaches can be taken for prototypical individual patient profiles with early knee OA. Animal models suggest that: (1) OA can progress even in the presence of fully recovered movement kinetics, kinematics and muscle activation patterns; (2) muscle weakness is an independent risk factor for the onset and possibly the rate of progression of knee OA; (3) onset and progression of OA are not related to body weight but appear to depend on the percentage of body fat. From studies in the human model, one could postulate that risk factors associated with progression of knee OA include genetic traits, preceding traumatic events, obesity, intensity of pain at baseline, static and dynamic joint malalignment and reduced muscle strength. Taken this into account, an individual can be identified as early knee OA at high risk for disease progression. A holistic patient-tailored management including education, supportive medication, weight loss, exercise therapy (aerobic, strengthening and neuromuscular) and behavioural approaches to improve self-management of early knee OA is discussed in individual prototypic patients. Secondary prevention of early knee OA provides a window of opportunity to slow down or even reverse the disease process. Yet, as the sheer number of patients early in the OA disease process is probably large, a more structured approach is needed to provide appropriate care depending on the patient's individual risk profile.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".