Potensi Injeksi Intra-artikular Sel Punca Mesenkimal sebagai Terapi Osteoartritis Lutut:
Bibliographic record
Abstract
Background: Osteoarthritis (OA) is a chronic multifactorial disease (inflammatory, mechanical, and metabolic) that is most often found in knee joints. Current OA therapy mostly uses NSAIDs as symptomatic treatment, with severe side-effects in long-term use. Therefore, a new causative therapy is needed that can minimize side effects and also has high efficacy. Mesenchymal stem cells (MSC) have promising potential for further research as a novel therapy for OA. Methods: This systematic review was conducted by including validated studies extracted from PubMed, Cochrane, ScienceDirect, dan ProQuest databases, using the keywords “knee osteoarthritis” and “mesenchymal stem cells” with a publication range of 2011-2021. RCT studies examining the use of intra-articular injection of MSC for knee OA were included. Outcome measures used were Visual Analog Scale (VAS), Western Ontario and McMaster University Osteoarthritis Index (WOMAC), MRI results (WORMS, cartilage volume, cartilage defect size), and side effects. Discussion: MSC can improve clinical assessment of OA, as seen from the significantly improved WOMAC and VAS scores and also radiological improvement, as seen from the improved WORMS scores, cartilage defects and volume. MSCs have anti-inflammatory and immunomodulatory potential, and are able to stimulate cartilage-like cells, which can assist in the recovery and prevention of further development of OA. Adverse effects are generally mild and self-limiting, therefore safe for human use. Conclusion: Intra-articular injection of MSC is potentially effective as a method of knee OA therapy with minimal side effects.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".