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Diagnostic and Research Techniques in Carpal Tunnel Syndrome

2019· review· en· W3005565828 on OpenAlexaff
Amanda Farias Zuniga, Peter J. Keir

Bibliographic record

VenueCritical Reviews in Biomedical Engineering · 2019
Typereview
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCarpal tunnel syndromeMedicineCarpal tunnelEntrapment NeuropathyMedian nerveNerve conductionNerve conduction studyWristSurgery

Abstract

fetched live from OpenAlex

Carpal tunnel syndrome is the most common neuropathy, costing upward of $2B USD annually in North America. Carpal tunnel syndrome is a result of chronic trauma to the median nerve, resulting in nerve damage, decreased conductivity of nerve impulses, and ultimately presents with clinical symptoms such as pain, tingling, numbness, and thumb muscle atrophy in severe cases. Although patient history, symptoms, and a nerve conduction study are the primary diagnosis tools, there are several techniques and tools that may be used to assess carpal tunnel syndrome and characterize the condition. The purpose of this critical review is to discuss the multitude of techniques that can be applied to study carpal tunnel syndrome, including Phalen's and Tinel's tests, nerve conduction study, Semmes-Weinstein monofilaments, laser Doppler flowmetry, pressure catheters, and ultrasound and magnetic resonance imaging. Additionally, this review discusses the reliability, sensitivity, and accuracy of these methods. A combination of these techniques may ultimately improve the accuracy in diagnosing carpal tunnel syndrome, especially in cases where nerve conduction study results are borderline or inconclusive by analyzing other aspects that may differently contribute to the development of carpal tunnel syndrome.

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)0.003
metaresearch head score (Gemma)0.035
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.834
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.124
GPT teacher head0.461
Teacher spread0.338 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations5
Published2019
Admission routes1
Has abstractyes

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