Chitosan-platelet-rich plasma implants improve rotator cuff repair in a large animal model: Pilot study
Bibliographic record
Abstract
Freeze-dried formulations of chitosan can be solubilized in platelet-rich plasma (PRP) to form injectable implants that are used as an adjunct treatment during surgical repair of the rotator cuff. The purpose of the current study was to assess chitosan-PRP implant residency, test safety, and assess efficacy over standard-of-care controls in a sheep model of rotator cuff repair. The infraspinatus tendon was transected unilaterally and immediately repaired with suture anchors in 22 skeletally mature ewes. In treatment groups, formulations containing chitosan, trehalose, and calcium chloride were solubilized with autologous leukocyte-rich PRP and injected at the tendon-bone interface and on top of the repaired site (1 mL or 2 mL doses). Implant residency was assessed histologically at 1 day. Outcome measures included MRI assessment at baseline, 6 weeks, and 12 weeks, histopathology and clinical pathology. Chitosan-PRP implants were resident at the injection site at 1 day and induced recruitment of polymorphonuclear cells. The tendon gap, which corresponds to the length of abnormally hyperintense tissue attached to the humeral head, was decreased by treatment with the 2 mL dose when compared to controls at 12 weeks on MRI images. Some histological features were improved by the 2 mL dose treatment compared to controls at 12 weeks. There was no treatment-specific effect on all standard safety outcome measures, which suggests high safety. This study provides preliminary evidence on the safety and efficacy of chitosan-PRP implants in a large animal model that could potentially be translated to a clinical setting.
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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 | 0.002 | 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 | 0.001 | 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".