Factors Influencing the Results in Matrix-Associated Autologous Chondrocyte Implantation: A 2 - 5 Year Follow-Up Study
Bibliographic record
Abstract
BACKGROUND: This study aimed to investigate the outcomes of matrix-associated autologous chondrocyte implantation (MACI) on the treatment of osteochondral lesions in the knee joint and to determine the factors affecting the functional results. METHODS: The study included 34 patients with a cartilage defect in the knee joint who were applied MACI® (GenzymeBiosurgery, Cambridge, Massachusetts, USA) technique between the years 2010 - 2015. The defect localizations and sizes, past surgeries were recorded. The clinical results were measured with Cincinnati and Lysholm scores. RESULTS: As a result of the repeated measures at postoperatively, it was found that the patients had increased Lysholm and Cincinnati functional scores in all follow-up periods (P = 0.0001). When the mean value of Lysholm and Cincinnati functional scores were assessed according to BMI group, no statistically significant difference was determined (P = 0.941 and P = 0.779). The measurements at 6 and 12 months of the follow-up indicated that the mean scores of the group with no concomitant pathologies were significantly higher than those of the group with concomitant pathologies. CONCLUSIONS: The MACI application provides good and stable outcomes for focal cartilage damage in young patients. In order to obtain significant results after autologous chondrocyte implantation, the selection of appropriate patients without concomitant pathologies is required.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".