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Record W2233022005 · doi:10.1097/aap.0000000000000331

The Second American Society of Regional Anesthesia and Pain Medicine Evidence-Based Medicine Assessment of Ultrasound-Guided Regional Anesthesia

2015· review· en· W2233022005 on OpenAlexafffund
Joseph M. Neal, Richard Brull, Jean-Louis Horn, Spencer S. Liu, Colin J. L. McCartney, Anahi Perlas, Francis V. Salinas, Ban C. H. Tsui

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2015
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of TorontoToronto Western HospitalUniversity of OttawaAlberta Hospital EdmontonUniversity of Alberta Hospital
FundersAudrey and Theodor Geisel School of Medicine at DartmouthUniversity of TorontoUniversity of AlbertaHospital for Special SurgeryDartmouth College
KeywordsMedicineUltrasoundRegional anesthesiaNerve blockJadad scaleEvidence-based medicineAnesthesiaRadiologySurgeryRandomized controlled trialAlternative medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVES: In 2009 and again in 2012, the American Society of Regional Anesthesia and Pain Medicine assembled an expert panel to assess the evidence basis for ultrasound guidance as a nerve localization tool for regional anesthesia. METHODS: The 2012 panel reviewed evidence from the first advisory but focused primarily on new information that had emerged since 2009. A new section was added regarding the accuracy and reliability of ultrasound for determining needle-to-nerve proximity. Jadad scores are used to rank study quality. Grades of recommendations consistent with their level of evidence are provided. RESULTS: The panel offers recommendations based on synthesis and analysis of literature related to (1) the technical capabilities of ultrasound equipment and its operators, (2) comparison of ultrasound to other methods of nerve localization with regard to block characteristics, (3) comparison of block techniques where ultrasound is the sole nerve localization modality, and (4) major complications. Assessment of evidence strength and recommendations are made for upper- and lower-extremity, truncal, neuraxial, and pediatric blocks. CONCLUSIONS: Scientific evidence from the past 5 years has clarified and strengthened our understanding of ultrasound-guided regional anesthesia as a nerve localization tool. High-level evidence supports ultrasound guidance contributing to superior characteristics with selected blocks, although absolute differences with the comparator technique are often relatively small (especially for upper-extremity blocks). The clinical meaningfulness of these differences is likely of variable importance to individual practitioners. The use of ultrasound significantly reduces the risk of local anesthetic systemic toxicity as well as the incidence and intensity of hemidiaphragmatic paresis, but has no significant effect on the incidence of postoperative neurologic symptoms. WHAT'S NEW IN THIS UPDATE?: This evidence-based assessment of ultrasound-guided regional anesthesia reviews findings from our 2010 publication and focuses on new meta-analyses, randomized controlled trials, and large case series published since 2009. New to this exercise is an in-depth analysis of the accuracy and reliability of ultrasound guidance for identifying needle-to-nerve relationships. This version no longer addresses ultrasound for interventional pain medicine procedures, because the growth of that field demands separate consideration. Since our 2010 publication, new information has either supported or strengthened our original conclusions. There is no evidence that ultrasound is inferior to alternative nerve localization methods.

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)0.048
metaresearch head score (Gemma)0.131
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.048
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.131
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.010
Bibliometrics0.0250.011
Science and technology studies0.0020.002
Scholarly communication0.0090.004
Open science0.0050.006
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0110.005

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 head0.122
GPT teacher head0.373
Teacher spread0.250 · 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 designSystematic review
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

Citations162
Published2015
Admission routes2
Has abstractyes

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