Scaphoid Bone Nonunions: Clinical and Functional Outcomes of Collagen/PGA Scaffolds and Cell-Based Therapy
Bibliographic record
Abstract
In this study, the procedure for treating the nonunion complication of scaphoid fractures using collagen/poly glycolic acid (CPGA) scaffolds with bone marrow mesenchymal stem cell (BM-MSC) therapy was adopted and compared with the commonly employed autologous bone tissue graft. With conducting a two-armed clinical trial, 10 patients with scaphoid nonunions were enrolled in this investigation. Patients were randomly assigned to two groups treated with (1) CPGA + cell therapy and (2) autologous iliac crest bone graft standard therapy. Treatment outcomes were evaluated three months after surgery, measuring the grip and pinch strengths and wrist range of motion, with two questionnaires: Patient-Rated Wrist Evaluation (PRWE) and Quick form of Disabilities of the Arm, Shoulder, and Hand (QDASH). We have also assessed the union rate using clinical and radiologic healing criteria one and three months post-operatively. Restorative effects of CPGA + cell therapy were similar to those of the autologous bone graft standard therapy, except for the grip strength ( P = 0.048) and QDASH score ( P = 0.044) changes, which were higher in the CPGA + cell therapy group. Three months following the surgery, radiographic images and computed tomography (CT) scans also demonstrated that the scaphoid union rate in the test group was comparable to that of scaphoids treated with the standard autograft method. Our findings demonstrate that the CPGA + cell therapy is a potential alternative for bone grafting in the treatment of bone nonunions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".