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Record W4402283266 · doi:10.25259/ijmsr_7_2024

Revisiting ultrasound assessment of median nerve in carpal tunnel syndrome: A review

2024· review· en· W4402283266 on OpenAlexaff
Vaishali Upadhyaya, Hema Choudur

Bibliographic record

VenueIndian Journal of Musculoskeletal Radiology · 2024
Typereview
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsMcMaster UniversityHamilton General Hospital
Fundersnot available
KeywordsCarpal tunnel syndromeMedian nerveMedicineUltrasoundCarpal tunnelAnatomyPhysical medicine and rehabilitationSurgeryRadiology

Abstract

fetched live from OpenAlex

Carpal tunnel syndrome (CTS), which occurs due to compression of the median nerve as it traverses the carpal tunnel at the level of the wrist joint, is the most common entrapment neuropathy. Conventionally, clinical evaluation and electrodiagnostic tests such as nerve conduction velocity and electromyography have been the mainstay of diagnosis in patients with clinically suspected CTS. In recent times, ultrasound (US) has become increasingly popular for diagnosing CTS. However, despite its widespread popularity, the criteria used for diagnosis vary widely. This paper aims to discuss multiple studies which evaluate the role of US in CTS and try to clarify which US criteria can be used with ease and accuracy in daily clinical practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.384
Teacher spread0.361 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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