Autologous chondrocyte implantation provides good long‐term clinical results in the treatment of knee osteoarthritis: a systematic review
Bibliographic record
Abstract
PURPOSE: To evaluate the mid- and long-term efficacy of autologous chondrocyte implantation (ACI) and matrix-assisted chondrocyte implantation (MACI) to treat patients with knee cartilage defects in the presence of osteoarthritis (OA). METHODS: PubMed and Cochrane databases were systematically searched for studies describing the treatment of knee OA with ACI or MACI (Kellgren-Lawrence (KL) ≥ 1, minimum follow-up 36 months). Results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines and included Lysholm, Western Ontario McMaster University and International Knee Documentation Committee scores. RESULTS: in the MACI group. ACI was mostly used for patients affected by KL I, whereas MACI for patients with KL II-IV. The data obtained from 235 patients (161 ACI, 74 MACI) showed that ACI and MACI sustained stable clinical improvements up to 11 and 15 years, respectively, with a failure rate of about 10% up to 11 years. Scarce biological details regarding chondrocyte implantation were reported. CONCLUSIONS: ACI and MACI procedures for the treatment of knee cartilage lesions associated to OA showed long-term success and allowed delaying arthroplasty. Additional trials reporting homogenous data and precise patient characterization are needed to conduct an effective literature meta-analysis and identify the clinical relevance of these procedures. LEVEL OF EVIDENCE: IV.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".