Growth differentiation factor 5 improves meniscal healing in a pilot study on rats
Bibliographic record
Abstract
Abstract Purpose Meniscus injuries are common, but failed repairs remain an issue. The aim of this study was to demonstrate, in an in vivo rat model, the ability of growth differentiation factor 5 (GDF5) to improve meniscal tear healing. Methods Eight Lewis rats (four females and four males) underwent radial tear of the medial meniscus on the right knee. There were two post‐operative treatments: the GDF5 group (n = 4) received 0.1 mg/mL of GDF5, and the saline group (n = 4) a saline injection. The eight left knees were the control without surgery. Sacrifice was six weeks post‐operatively. The GDF5 and saline groups were compared according to histology and meniscus healing score (MHS) in different zones: red‐red (R‐R), red‐white (R‐W) and white‐white (W‐W). Results In the R‐R zone, the median [interquartile range, IQR] MHS was 2.5 [2–3] in the GDF5 group and 2 [1.25–2] in the saline group (p = 0.200), and in the W‐R zone it was 2 [2–2.75] for GDF5 and 1 [1–1.75] for saline (p = 0.047). There was no difference in the W‐W zone (median MHS under one; p = 0.686). Regardless of groups, median [IQR] MHS in R‐R (2 [2–2.75]) and R‐W zones (2 [1–2]) were significantly higher (p < 0.001) than in the W‐W zone (0 [0–1]). MHS intraclass correlation coefficient inter‐observer was 0.88 and intra‐observer was 0.90. Conclusions GDF5 increases meniscal healing, especially in the R‐W zone, although the W‐W zone remains challenging. GFD5 is a promising factor for improving meniscus healing. The small sample size and absence of biomechanical evaluation are limitations that warrant caution when interpreting these findings. Further studies with larger sample sizes in larger animal models, combined with meniscal repair, are required to confirm these preliminary results. Study Design Animal laboratory study. Level of Evidence Level V, animal study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".