Bibliographic record
Abstract
INTRODUCTION: Osteoarthritis is a heterogeneous joint disorder that lacks a clinically meaningful disease modifying drug. Animal models have been beneficial in understanding basic joint pathology and providing rationale for future clinical trials on identified targets. This review aims to discuss promising therapeutic targets of osteoarthritis that are currently in animal studies or early clinical trials. AREAS COVERED: PubMed was searched for articles published between 2017 and 2021 with the following terms: (osteoarthritis AND autophagy) OR (osteoarthritis AND senescence) OR (osteoarthritis AND TGFbeta) OR (osteoarthritis AND EGFR) OR (osteoarthritis AND Wnt/β-catenin) OR (osteoarthritis AND inflammation). Specific targets include the PI3/AKT/mTOR pathway, epidermal growth factor receptor, Toll-like receptors, and inflammatory interleukins, among others. EXPERT OPINION: In reviewing these targets, it is clear that the field of therapeutic targets for osteoarthritis has grown tremendously. We have gained a better understanding of previously identified targets, identified new targets, and have the opportunity to explore enhanced drug delivery via viral vectors. Regardless, translation to clinical benefits is still lacking in most cases. We propose that this may be due to the heterogeneous nature of the disease, lack of early diagnostic markers, mismatched preclinical animal models and clinical populations, and the complex role of many targets of interest.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".