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Record W3216221491 · doi:10.3390/pharmaceutics13111955

Chitosan–Platelet-Rich Plasma Implants Improve Rotator Cuff Repair in a Large Animal Model: Pivotal Study

2021· article· en· W3216221491 on OpenAlexaff
Anik Chevrier, Mark Hurtig, Marc Lavertu

Bibliographic record

VenuePharmaceutics · 2021
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of GuelphPolytechnique Montréal
Fundersnot available
KeywordsPlatelet-rich plasmaChitosanRotator cuffMedicineHistopathologyTendonRotator cuff injuryMagnetic resonance imagingSurgeryPathologyPlateletInternal medicineRadiologyChemistry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.389
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2021
Admission routes1
Has abstractyes

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