New developments in the treatment of osteoarthritis – focus on biologic agents
Bibliographic record
Abstract
Osteoarthritis (OA) is one of the most common diseases around the world. Medical, social, and financial consequences oblige clinicians, surgeons, and researchers to focus on finding the best treatment option, to eradicate and stop this degenerative joint disease, in order to avoid surgical options which in many instances are over-indicated. Noninvasive treatments, such as anti-inflammatory drugs, physiotherapy, orthotic devices, dietary supplements, have demonstrated lack of effectiveness. The possibility to perform intra-articular injections with hyaluronic acid, corticosteroids, or the newest but criticized treatment based on platelet-rich plasma (PRP) has changed the management of OA disease. The use of PRP has led to many differences in treatment since there is a lack of consensus about protocols, indications, number of doses, cost-effectiveness, and duration of the treatment. Many publications have suggested efficacy in tendon injuries, but when PRP has been indicated to treat cartilage injuries, things are more inconsistent. Some authors have reported their experience treating OA with PRP, and it seems that, if well indicated, it is an option as a supplementary therapy. Therefore, we need to understand that OA is a mechanical disease which not only produces changes in radiographs, but also affects the quality of life. Pathogenesis of OA has been well explained, providing us new knowledge and future possibilities to improve the clinical approach. From basic science to surgery, there is a great field we all need to contribute to, because the general population is aging and total joint replacements should not be the only solution for OA. So herein is an actual review of the developments for treating OA with biologics, intended to be useful for the population inside orthopedics who could be called bio-orthopedists, since OA is a molecular homeostasis disbalance between catabolism and anabolism triggered by mechanical stress.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".