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Record W3014647297 · doi:10.1002/mus.26878

Ultrasound analysis of cervical paraspinal muscles for needle EMG examination

2020· article· en· W3014647297 on OpenAlexaff
Serge Mrkobrada

Bibliographic record

VenueMuscle & Nerve · 2020
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsMedicineUltrasoundAnatomyBlood flowDoppler ultrasoundNuclear medicineLaminaRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Ultrasound was used to determine optimal needle insertion parameters and assess the vasculature of paraspinal muscles at C5-T1 spinal levels across patients with different body mass indices (BMIs). METHODS: Thirty patients underwent ultrasound examination of the cervical paraspinal muscles at the C5-T1 levels. Images were analyzed to determine the optimal distance and angle of needle insertion to reach the base of the right lamina. Color and spectral Doppler analysis were used to identify and map paraspinal blood vessels. RESULTS: Mean distances and angles varied from 35.1 mm and 17.27 degrees for the low BMI group at C5 to 65.1 mm and 9.85 degrees for the high BMI group at T1. Paraspinal blood vessel mapping revealed a random distribution of vasculature. CONCLUSIONS: Longer distances and steeper angles of needle insertion are required for patients with higher BMIs. Cervical paraspinal arteries vary in distribution and can be visualized with ultrasound.

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 score0.789
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.038
GPT teacher head0.294
Teacher spread0.257 · 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
Published2020
Admission routes1
Has abstractyes

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