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Record W4414353165 · doi:10.1097/gox.0000000000007104

Effect of Platelet-rich Plasma and Brief Electrical Stimulation Following Facial Nerve Neurorrhaphy in a Rat Model

2025· article· en· W4414353165 on OpenAlexaff
Janelle Sloychuk, Jin Soo Song, Lin Zhu, Adrian Mendez, Jill Querney, David W. J. Côté

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsWestern UniversityUniversity of Alberta
Fundersnot available
KeywordsRat modelExpeditingStimulationFacial nerveAntidromicElectric stimulation

Abstract

fetched live from OpenAlex

Background: With ongoing investigations to improve facial nerve recovery after injury, brief electrical stimulation (BES) and platelet-rich plasma (PRP) application have become promising modalities. The usage of combined modalities has not yet been investigated. We aimed to evaluate differences in facial nerve regeneration between rats receiving BES (group 1) and rats receiving BES plus PRP (group 2) following transection and primary neurorrhaphy. Methods: This was a prospective randomized animal study of 2 groups of 6 rats that underwent facial nerve transection and repair at the main trunk of the nerve. Both groups received BES on postoperative day 0 for 1 hour using an implantable stimulation device, with group 2 additionally receiving concurrent PRP. The primary measure of whisker amplitude was recorded from 2 to 8 weeks postoperatively. Results: At week 2, the average whisker amplitude for groups 1 and 2 was 6.5 and 2.0 degrees, respectively. At week 4, group 1 remained unchanged at 6.1 degrees, whereas group 2 improved to 10.1 degrees. By 6 weeks, average amplitudes were 8.2 and 14.0 degrees, respectively. By week 8, measures were 9.8 and 16.7 degrees, respectively. All results were statistically significant ( P < 0.05). Conclusions: Application of BES and PRP following neurorrhaphy in a rat model yields a greater effect than BES alone in expediting facial nerve recovery.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.571
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.288
Teacher spread0.270 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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