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Record W2092805891 · doi:10.1097/aap.0000000000000155

Evidence Base for the Use of Ultrasound for Upper Extremity Blocks

2014· review· en· W2092805891 on OpenAlexaff
Stephen Choi, Colin J. L. McCartney

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2014
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreHealth Sciences Centre
Fundersnot available
KeywordsMedicineBase (topology)UltrasoundPhysical medicine and rehabilitationRadiology

Abstract

fetched live from OpenAlex

This article reviews and summarizes randomized, controlled studies that have assessed ultrasound (US) guidance for brachial plexus blocks in comparison with other nerve localization methods as well as those that have compared different US-guided brachial plexus block techniques. Both PubMed and EMBASE databases were searched using the MeSH terms anesthetic technique, brachial plexus, and ultrasound. Studies were included if they had randomized allocation comparing US with another conventional nerve localization technique or if they compared 2 different US-guided techniques, such as single versus multiple injections. Each study was classified as a categorical outcome as being supportive, unclear, or negative for the use of US. These were compared with χ analysis with the null hypothesis that US provides no benefit for brachial plexus blocks. Forty-seven studies met the inclusion criteria, and 29 compared US guidance to landmark or peripheral nerve stimulation techniques. Our analysis of the literature supports the use of US over other nerve localization techniques as being beneficial for several block performance outcomes including block performance time, reducing the number of needle passes and the incidence of vascular puncture, shortening sensory block onset time, and improving block success.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.201
GPT teacher head0.354
Teacher spread0.153 · 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 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

Citations40
Published2014
Admission routes1
Has abstractyes

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