Chitosan–Platelet-Rich Plasma Implants Improve Rotator Cuff Repair in a Large Animal Model: Pivotal Study
Bibliographic record
Abstract
The purpose of this study was to assess the safety and efficacy of chitosan–platelet-rich plasma (PRP) hybrid implants used as an adjunct to surgical rotator cuff repair in a pivotal Good Laboratory Practice (GLP)-compliant study. The infraspinatus tendon was transected in 48 skeletally mature ewes and repaired with a transosseous-equivalent (TOE) technique. In the two treatment groups, a chitosan–PRP solution was injected at the footprint between the tendon and the bone and on top of the repaired site (2 mL or 3 mL doses, n = 12 per group). To further assess chitosan safety, a chitosan–water solution was injected at the same sites (3 mL, n = 12). Outcome measures included Magnetic Resonance Imaging (MRI) assessment and clinical pathology at 3 months and 6 months and histopathology at 6 months. The tendon gap was decreased at 3 months on MRI images and certain histopathological features were improved at 6 months by chitosan–PRP treatment compared to controls. The group treated with chitosan–water was not different from controls. Chitosan–PRP treatment induced no negative effects in the sheep, which suggests high safety. This study provides further evidence on the safety and efficacy of chitosan–PRP for rotator cuff repair augmentation, which could eventually be used in a clinical setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".