Osteoarthritis as an Umbrella Term for Different Subsets of Humans Undergoing Joint Degeneration: The Need to Address the Differences to Develop Effective Conservative Treatments and Prevention Strategies
Bibliographic record
Abstract
Osteoarthritis (OA) of joints such as the knee and hip are very prevalent, and the number of individuals affected is expected to continue to rise. Currently, conservative treatments after OA diagnosis consist of a series of increasingly invasive interventions as the degeneration and pain increase, leading very often to joint replacement surgery. Most interventions are focused on alleviating pain, and there are no interventions currently available that stop and reverse OA-associated joint damage. For many decades OA was considered a disease of cartilage, but it is now considered a disease of the whole multi-tissue joint. As pain is the usual presenting symptom, for most patients, it is not known when the disease process was initiated and what the basis was for the initiation. The exception is post-traumatic OA which results from an overt injury to the joint that elevates the risk for OA development. This scenario leads to very long wait lists for joint replacement surgery in many jurisdictions. One aspect of why progress has been so slow in addressing the needs of patients is that OA has been used as an umbrella term that does not recognize that joint degeneration may arise from a variety of mechanistic causes that likely need separate analysis to identify interventions unique to each subtype (post-traumatic, metabolic, post-menopausal, growth and maturation associated). A second aspect of the slow pace of progress is that the bulk of research in the area is focused on post-traumatic OA (PTOA) in preclinical models that likely are not clearly relevant to human OA. That is, only ~12% of human OA is due to PTOA, but the bulk of studies investigate PTOA in rodents. Thus, much of the research community is failing the patient population affected by OA. A third aspect is that conservative treatment platforms are not specific to each OA subset, nor are they integrated into a coherent fashion for most patients. This review will discuss the literature relevant to the issues mentioned above and propose some of the directions that will be required going forward to enhance the impact of the research enterprise to affect patient outcomes.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".