Clinical and Quality of Life Outcomes of a Combined Synthetic Scaffold and Autogenous Tissue Graft Procedure for Articular Cartilage Repair in the Knee
Bibliographic record
Abstract
Abstract Background: Injuries to the articular cartilage of the knee often fail to heal properly, due to the hypocellular and avascular nature of this tissue. Subsequent disability can limit participation in sports and decrease quality of life. Subchondral bone perforations are used for the treatment of small defects. Larger lesions filling out the central portion becomes difficult, and scaffolds can be used as adjuvants, providing a matrix onto which the defect can be filled in completely and also autogenous cartilage grafts can be combined, acting as an inducer and improving healing quality, all in a single procedure.Methods: Evaluate the clinical and quality of life outcomes of patients with articular cartilage lesions of the knee undergoing repair via a microfracture technique combined with a synthetic scaffold and autogenous cartilage graft, with transosseous sutures and fibrin glue fixation, at 12 months of follow-up. Secondarily, assess whether combined procedures, previous surgical intervention, traumatic aetiology, lesion location, and age affect outcomes. Observational study of adult patients (age 18–66 years) with symptoms consistent with chondral or osteochondral lesions, isolated or multiple, ICRS grade III/IV, 2–12 cm2 in size. Patients with corrected angular deviations or instabilities were included. Those with BMI > 40 kg/m2, prior total or subtotal (> 30%) meniscectomy, second-look procedures, and follow-up < 6 months were excluded. Pain (VAS), physical activity (IKDC), osteoarthritis (WOMAC), and general quality of life (SF-36) were assessed.Results: 64 procedures were included, comprising 60 patients. There was significant improvement (P < 0.05) in VAS (5.92 to 2.37), IKDC score (33.44 to 56.33), and modified WOMAC score (53.26 to 75.93) after surgery. The SF-36 showed significant improvements in the physical and mental domains (30.49 to 40.23 and 46.43 to 49.84 respectively; both P < 0.05).Conclusions: Combination of microfractures, autogenous crushed cartilage graft, synthetic scaffold, and transosseous sutures with fibrin glue provides secure fixation for treatment of articular cartilage lesions of the knee. At 12-month follow-up, function had improved by 20 points on the IKDC and WOMAC, and quality of life by 10 points on the SF-36. Age > 45 years had a negative impact on outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".