Freeze-dried chitosan-platelet-rich plasma implants improve supraspinatus tendon attachment in a transosseous rotator cuff repair model in the rabbit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rotator cuff tears result in shoulder pain, stiffness, weakness and loss of motion. After surgical repair, high failure rates have been reported based on objective imaging and it is recognized that current surgical treatments need improvement. The aim of the study was to assess whether implants composed of freeze-dried chitosan (CS) solubilized in autologous platelet-rich plasma (PRP) can improve rotator cuff repair in a rabbit model. Complete tears were created bilaterally in the supraspinatus tendon of New Zealand White rabbits ( n = 4 in a pilot feasibility study followed by n = 13 in a larger efficacy study), which were repaired using transosseous suturing. On the treated side, CS-PRP implants were injected into the transosseous tunnels and the tendon itself, and healing was assessed histologically at time points ranging from one day to two months post-surgery. CS-PRP implants were resident within transosseous tunnels and adhered to tendon surfaces at one day post-surgery and induced recruitment of polymorphonuclear cells from 1 to 14 days. CS-PRP implants improved attachment of the supraspinatus tendon to the humeral head through increased bone remodelling at the greater tuberosity and also inhibited heterotopic ossification of the supraspinatus tendon at two months. In addition, the implants did not induce any detectable deleterious effects. This preliminary study provides the first evidence that CS-PRP implants could be effective in improving rotator cuff tendon attachment in a small animal model.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it