New and emerging treatments for osteoarthritis management: will the dream come true with personalized medicine?
Bibliographic record
Abstract
INTRODUCTION: Osteoarthritis (OA) is a dynamic process involving the main tissues of the joint for which a global approach should be considered. No disease-modifying OA drug (DMOAD) has yet been approved. New therapeutic strategies are needed that would be cost effective by reducing the need for pharmacological interventions and surgical management while targeting specific pathways leading to OA. The treatment landscape of OA is about to change based on new agents having shown some structural effects and emerging therapies with DMOAD effects. AREAS COVERED: In this review based on a Medline (via PubMed) search, promising new and emerging therapies with a potential structural effect (DMOAD) will be discussed including growth factors, platelet-rich plasma, autologous stem cells, bone remodeling modulators, cytokine inhibition, gene therapy, and RNA interference. EXPERT OPINION: DMOAD development should focus on targeting some phenotypes of OA patients evidenced with sensitive techniques such as magnetic resonance imaging, as a single treatment will unlikely be appropriate for all OA patients. This will allow the development of DMOADs based on personalized medicine. An exciting new era in DMOAD development is within reach, provided future clinical trials are sufficiently powered, systematically designed, use the appropriate evaluation tools, and target the appropriate categories of OA patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".