Clinical efficacy and safety of the combination of mesenchymal stem cells and scaffolds in the treatment of knee osteoarthritis: Protocol for systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Mesenchymal stem cells (MSCs) injection combined scaffolds for knee osteoarthritis (OA) is a relatively new treatment for knee OA and has not yet gained popularity. So, the effectiveness, safety is worthy to be explored. We performed a protocol for systematic review and meta-analysis to evaluate the efficacy and safety of the combination of MSCs and scaffolds in the treatment of knee OA. METHODS: A literature search was performed in October 2022 without restriction to regions, publication types or languages. The primary sources were the electronic databases of PubMed, EMBASE, Cochrane Library, Web of Science and the ClinicalTrials.gov. Risk of bias was assessed using the Cochrane Collaboration's risk of bias tool for randomized controlled trials. Statistical analyses were performed utilizing Review Manager 5 (The Nordic Cochrane Center, Copenhagen, Denmark). RESULTS: Visual analog scale score, Western Ontario and McMaster Universities Osteoarthritis Index, Lysholm knee scale and adverse events will be assessed. CONCLUSION: The systematic review will provide evidence to assess the effectiveness and safety of MSCs combined scaffolds for the treatment of knee OA.
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 | 0.048 | 0.074 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 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".