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Record W2075141945 · doi:10.1016/s1098-7339(03)00189-5

Applying ultrasound imaging to interscalene brachial plexus block

2003· article· en· W2075141945 on OpenAlexaff
Vincent Chan

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2003
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineBrachial plexusUltrasoundBrachial plexus blockNerve blockLocal anestheticShoulder surgeryRadiologyAnesthesia

Abstract

fetched live from OpenAlex

OBJECTIVE: Previous studies have examined ultrasound-assisted brachial plexus blocks, but few have applied this imaging technology to the interscalene region. We report a case of interscalene brachial plexus block using ultrasound guidance to show the clinical usefulness of this technology. CASE REPORT: A nerve stimulator-guided interscalene block was attempted for arthroscopic shoulder surgery but failed. Subsequent nerve localization was accomplished by ultrasound imaging using a high-frequency probe (5-12 MHz) and the Philips ATL HDI 5000 unit. Ultrasound showed nerves between the scalene muscles, block needle movement at the time of advancement, and local anesthetic spread during injection. Interscalene block was successful after 1 attempt of nerve localization and needle placement. CONCLUSIONS: Advanced ultrasound technology is useful for nerve localization and can generate brachial plexus images of high resolution in the interscalene groove, guide block needle placement and advancement in real time to targeted nerves, and assess adequacy of local anesthetic spread at the time of injection. Ultrasound imaging guidance can potentially improve success during interscalene brachial plexus block.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.261
Teacher spread0.243 · 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 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

Citations123
Published2003
Admission routes1
Has abstractyes

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