Translating CRISPR-Cas Systems into musculoskeletal medicine and orthopaedics: a scoping review protocol
Bibliographic record
Abstract
Abstract Background Musculoskeletal diseases are a leading cause of global disability and healthcare burden, yet traditional orthopaedic procedures often fail to address molecular drivers of degenerative and genetic conditions. CRISPR-Cas Systems, a precise and programmable genome-editing technology, show promise in preclinical musculoskeletal models, ranging from gene knockouts in osteoarthritis to mutation correction in skeletal dysplasia. Despite growing interest, no comprehensive synthesis exists to map how CRISPR-Cas Systems are being applied in orthopaedic research. This scoping review aims to fill that gap. Methods This protocol will be registered with the Open Science Framework and follows PRISMA-ScR guidelines and the Arksey & O’Malley framework. MEDLINE, Embase, Scopus, Web of Science, and grey literature sources from 2005 onward will be searched. Inclusion criteria encompass original research using CRISPR-Cas Systems in human, animal, or in vitro musculoskeletal models. Two reviewers will independently screen titles, abstracts, and full texts using Covidence software. Data extraction will be standardized and performed in duplicate. Extracted variables include study design, model system, target tissue, gene-editing approach, delivery system, and reported outcomes. Results will be synthesized descriptively and thematically. Expected Results We anticipate mapping the evolution and diversity of CRISPR-Cas Systems applications across musculoskeletal tissues (bone, cartilage, tendon, muscle), highlighting domains such as tissue regeneration, gene correction, and disease modeling. Delivery strategies (viral vectors, nanoparticles) and translational challenges, including off-target effects and delivery barriers, will be summarized. Conclusions This will be the first scoping review to systematically characterize the role of CRISPR-Cas Systems in musculoskeletal medicine. Findings will inform researchers, clinicians, and policymakers, helping to guide future translational research and accelerating the integration of gene editing into clinical practice.
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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 | 0.089 | 0.101 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.031 | 0.022 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.012 |
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".