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Record W1555979211 · doi:10.3171/jns/2008/109/11/0955

Clinical application of sensory protection of denervated muscle

2008· article· en· W1555979211 on OpenAlexafffund
James R. Bain, Yaniv Hason, Karen Veltri, Margaret Fahnestock, Caroline Quartly

Bibliographic record

VenueJournal of neurosurgery · 2008
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMedicineDenervationElectromyographySensory systemNeurolysisSciatic nerveProprioceptionSensory lossSurgeryGastrocnemius muscleAnesthesiaTibial nerveCommon peroneal nerveTibialis anterior muscleSensory nerveAnatomyPhysical medicine and rehabilitationStimulationNeuroscienceSkeletal muscleInternal medicine

Abstract

fetched live from OpenAlex

Following proximal peripheral nerve injury, motor recovery is often poor due to prolonged muscle denervation and loss of regenerative potential. The transfer of a sensory nerve to denervated muscle results in improved functional recovery in experimental models. The authors here report the first clinical case of sensory protection. Following a total hip arthroplasty, this patient experienced a complete sciatic nerve palsy with no recovery at 3 months postsurgery and profound denervation confirmed electrodiagnostically. He underwent simultaneous neurolysis of the sciatic nerve and saphenous nerve transfers to the tibialis anterior branch of the peroneal nerve and gastrocnemius branch from the tibial nerve. He noted an early proprioceptive response. Electromyography demonstrated initially selective amelioration of denervation potentials followed by improved motor recovery in sensory protected muscles only. The patient reported clinically significant functional improvements in activities of daily living. The authors hypothesize that the presence of a sensory nerve during muscle denervation can improve functional motor 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.071
GPT teacher head0.336
Teacher spread0.265 · 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 designObservational
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

Citations40
Published2008
Admission routes2
Has abstractyes

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