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Record W2025989632 · doi:10.1055/s-2001-12996

Contemporary Management of Peripheral Nerve Trauma

2001· article· en· W2025989632 on OpenAlexaff
John K. Ratliff, Line Jacques, David G. Kline

Bibliographic record

VenueSeminars in Neurosurgery · 2001
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMedicinePeripheral nervePeripheral nerve injuryNerve injuryMagnetic resonance imagingNeuroscienceSurgeryRadiologyAnatomyPsychology

Abstract

fetched live from OpenAlex

Peripheral nerve injury remains a significant cause of disability. Injuries often occur in young, otherwise healthy individuals. Currently, civilians compose the majority of evaluated cases, but significant injuries continue to occur during wartime. Some lesions recover spontaneously and others are helped by surgical repair; still others have persistent neurologic deficits even with optimal management. The evolution of imaging, predominantly the widespread availability of magnetic resonance imaging (MRI) and development of surface coils specific for nerve, may improve preoperative assessment of nerve damage. Intraoperative electrophysiologic assessment of nerve lesions is another advance that continues to evolve. Advances in neurotrophin physiology and better understanding of nerve regeneration have yet to yield significant improvements in repair but offer promise for the future. We review the contemporary management of peripheral nerve injury, focusing on treatment paradigms developed at the Louisiana State University Health Science Center, but also highlighting promising areas of research. The foundations for management of traumatic nerve injury remain constant. These include a thorough history and physical examination, appropriate radiographic assessment, electrophysiology assessment, and operative exploration when indicated. KEYWORD Peripheral nerve - nerve injury - nerve action potential

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 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 score0.068
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.046
GPT teacher head0.280
Teacher spread0.234 · 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
Published2001
Admission routes1
Has abstractyes

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