Title Freeze-Dried Chitosan-Thrombin-Platelet-Rich Plasma Implants Improve Supraspinatus Tendon Regeneration in a Rabbit Rotator Cuff Repair Model
Bibliographic record
Abstract
Rotator cuff tears stand as the most prevalent shoulder condition prompting medical intervention. Failure rates as high as 60% have been reported after surgery, indicating the need for improved treatments. The aim of this study was to confirm whether an implant composed of freeze-dried chitosan and thrombin rehydrated in autologous platelet-rich plasma (CS-FIIa-PRP) is effective in improving supraspinatus tendon regeneration in a New Zealand white rabbit model. Complete tears of the supraspinatus tendon were created bilaterally, then repaired using transosseous sutures. On the treated shoulder, CS-FIIa-PRP implants were injected both in the transosseous tunnels and on the tendon at the repaired site. Animals were sacrificed after 1 day ( n = 1), 2 weeks ( n = 6), 4 weeks ( n = 6), and 3 months ( n = 6). CS-FIIa-PRP implants were observed in the tunnels and on the tendon surface up to 4 weeks postsurgery and did not induce any deleterious effects. Using those implants in addition to sutures improved the macroscopic attachment of tendon to the humeral head at early time points (2 and 4 weeks), led to a faster improvement of the tendons’ mechanical properties ( p < 0.05), and inhibited the development of heterotopic ossification ( p < 0.01). Rotator cuff regeneration using CS-FIIa-PRP implants is, therefore, a promising treatment for rotator cuff defects when combined with sutures.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | medium |
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".