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Record W2526234097 · doi:10.1017/cjn.2016.302

Outcome Comparison of Nerve Transfer with Different Donor Nerves in a Rat Model

2016· article· en· W2526234097 on OpenAlexvenueno aff
Xiaotian Jia, Cong Yu, Jianyun Yang

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2016
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBrachial plexusPhrenic nerveNerve rootAccessory nerveAnesthesiaMusculocutaneous nerveAvulsionAnatomyCervical NerveSurgeryRespiratory system

Abstract

fetched live from OpenAlex

OBJECTIVE: The phrenic nerve and the contralateral seventh cervical (C7) nerve root are the most commonly used donor nerves in the treatment of total brachial plexus avulsion. The aim of this study was to determine if the phrenic nerve or the contralateral C7 nerve root yields a superior outcome for nerve transfer. METHODS: A total of 60 Sprague-Dawley rats were randomly assigned to 1 of 3 groups. In Group A the phrenic nerve was used as the donor nerve; in Group B the contralateral C7 nerve root nerve was used as the donor nerve; in Group C the nerve was directly sutured. The results of behavioral assessment, electrophysiology, histology, nerve fiber count and muscle weight at 24 weeks postoperatively were recorded. RESULTS: Group A showed a faster recovery time compared to Group B; however Group B showed a better functional recovery at the final outcome assessment compared to Group A. CONCLUSION: The contralateral C7 nerve root was better as the donor nerve for nerve transfer in the treatment of total brachial plexus avulsion.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.311
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2016
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicNerve Injury and RehabilitationFrench-language works237,207