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Record W4389038140 · doi:10.1002/mus.28008

Comparison of electrodiagnostic findings in acute traumatic versus chronic non‐traumatic ulnar neuropathy at the elbow

2023· article· en· W4389038140 on OpenAlexaff
Lawrence R. Robinson, P. L. Broadhurst, Alex Wasserman

Bibliographic record

VenueMuscle & Nerve · 2023
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsSpinal Cord Injury AlbertaBritish Columbia Rehabilitation FoundationToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsMedicineElbowElectromyographyEtiologyUlnar nerveSurgeryUlnar neuropathyAnesthesiaPhysical therapyPhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION/AIMS: A common concept is that traumatic nerve injuries are more likely axonal, and that compressive neuropathies are more likely demyelinating. The purpose of this study was to compare traumatic versus non-traumatic ulnar neuropathy at the elbow (UNE) to look for electrodiagnostic differences between the two groups. METHODS: A retrospective 3 year review of UNE patients at two academic health science centers was conducted. Patients were grouped into acute traumatic UNE versus chronic non-traumatic UNE based on clinical history. Electrodiagnostic measurements were compared between the two groups. RESULTS: There were 50 subjects with acute traumatic UNE and 41 with chronic non-traumatic UNE. Mean age and sex distribution were similar but those with traumatic UNE had a 7 month duration of symptoms, while those with chronic UNE had 29 month duration (p < .001). All electrodiagnostic measurements were similar between the two groups including compound muscle action potential amplitudes, motor conduction velocities, frequency of conduction block, sensory nerve studies, and needle electromyography. DISCUSSION: We did not find a difference between the two groups. One should not make inferences regarding acuity or etiology based on electrodiagnostic features alone.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.040
GPT teacher head0.338
Teacher spread0.298 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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