Therapeutics of the future: Navigating the pitfalls of extracellular vesicles research from an osteoarthritis perspective
Bibliographic record
Abstract
Extracellular vesicles have gained wide momentum as potential therapeutics for osteoarthritis, a highly prevalent chronic disease that still lacks an approved treatment. The membrane-bound vesicles are secreted by all cells carrying different cargos that can serve as both disease biomarkers and disease modifiers. Nonetheless, despite a significant peak in research regarding EVs as OA therapeutics, clinical implementation seems distant. In addition to scalability and standardization challenges, researchers often omit to focus on and consider the proper tropism of the vesicles, the practicality and relevance of their source, their low native therapeutic efficacy, and whether they address the disease as a whole. These considerations are necessary to better understand EVs in a clinical light and have been comprehensively discussed and ultimately summarized in this review into a conceptualized framework termed the nanodiamond concept. Future perspectives are also discussed, and alternatives are presented to address some of the challenges and concerns.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".