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Record W4392735103 · doi:10.1080/21681163.2024.2312171

Ultrasound image quality of the carpal tunnel during a pinch grip: effects of coupling media and wrist position

2024· article· en· W4392735103 on OpenAlexafffund
Denise Balogh, Michelle Campbell, Aaron M. Kociolek

Bibliographic record

VenueComputer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · 2024
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsNipissing UniversityUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWristPosition (finance)UltrasoundCoupling (piping)Carpal tunnelCarpal tunnel syndromeMedicineMaterials sciencePhysical medicine and rehabilitationAnatomySurgeryRadiologyComposite material

Abstract

fetched live from OpenAlex

High quality ultrasound is required for evaluating carpal tunnel dynamics. The purpose of this study was to subjectively assess ultrasound video quality of the carpal tunnel with two commonly used coupling media (coupling gel, 7 mm standoff pad) in two wrist positions (0° neutral, 30° flexion) over the entire time course of a pinch grip. Seventeen participants completed a pinch grip while their carpal tunnel was scanned with ultrasound. The ultrasound videos were graded by two independent reviewers using 10-point scales encompassing median nerve detail, resolution, and total video quality. Intra- and inter-rater reliability were highest for median nerve detail (ICCs>.85). Ultrasound videos in the 0° neutral wrist condition resulted in significantly greater ratings of nerve detail compared to those in 30° wrist flexion. Future studies may benefit from a more customised approach to standoff pad thickness during dedicated analysis of the median nerve in a neutral wrist position.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.331
Teacher spread0.321 · 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 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

Citations2
Published2024
Admission routes2
Has abstractyes

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