Histological Analysis of Cartilage Defects Repaired with an Autologous Human Stem Cell Construct 48 Weeks Postimplantation Reveals Structural Details Not Detected by T2-Mapping MRI
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to elucidate the efficacy of T2-mapping MRI and correlation with histology for the evaluation of tissue repair quality following the first-in-human implantation of an autologous tissue engineered construct. DESIGN: We directly compared the results of T2-mapping MRI of cartilage repair tissue with the histology of a biopsy specimen from the corresponding area at 48 weeks postoperatively in 5 patients who underwent the implantation of a scaffold-free tissue-engineered construct generated from autologous synovial mesenchymal stem cells to repair an isolated cartilage lesion. T2 values and histological scores were compared at each of 2 layers of equally divided halves of the repair tissue (upper and lower zones). RESULTS: Histology showed that the repair tissue in the upper zone was dominated by fibrous tissue and the ratio of hyaline-like matrix increased with the depth of the repair tissue. There were significant differences between upper and lower zones in histological scores. Conversely, there were no detectable statistically significant differences in T2 value detected among zones of the repair tissue, but zonal differences were detected in corresponding healthy cartilage. Accordingly, there were no correlations detected between histological scores and T2 values for each repair cartilage zone. CONCLUSION: Discrepancies in the findings between T2 mapping and histology suggest that T2 mapping was limited in ability to detect details in the architecture and composition of the repair cartilage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".