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Record W2470930738 · doi:10.3389/fneur.2016.00111

Distinguishing Radiculopathies from Mononeuropathies

2016· article· en· W2470930738 on OpenAlexaff
Jennifer Robblee, Hans Katzberg

Bibliographic record

VenueFrontiers in Neurology · 2016
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMononeuropathyMedicineNerve biopsyHyporeflexiaPhysical medicine and rehabilitationNeurologyDifferential diagnosisCarpal tunnel syndromeSensory systemWristPeripheral neuropathySurgeryWeaknessPsychologyNeurosciencePathology

Abstract

fetched live from OpenAlex

Identifying “where is the lesion” is particularly important in the approach to the patient with focal dysfunction where a peripheral localization is suspected. This article outlines a methodical approach to the neuromuscular patient in distinguishing focal neuropathies versus radiculopathies, both which are common presentations to the neurology clinic. This approach begins with evaluation of the sensory examination to determine whether there are irritative or negative sensory signs in a peripheral nerve or dermatomal distribution. This is followed by evaluation of deep tendon reflexes to evaluate if differential hyporeflexia can assist in the two localizations. Finally, identification of weak muscle groups unique to a nerve or myotomal pattern in the proximal and distal extremities can most reliably assist in a precise localization. The article concludes with an application of the described method to the common scenario of distinguishing radial neuropathy versus C7 radiculopathy in the setting of a wrist drop and provides additional examples for self-evaluation and reference. The current approach uses the the basics of the neurological examination in a unique way that can be used by a various clinicians to localize these common neuromuscular processes. The learner can then build on this structured approach when encountering more complex neuromuscular presentations, including localizing less common mononeuropathies or plexopathies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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