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Record W1997754684 · doi:10.1097/aap.0b013e3181faa11c

A Randomized Comparison Between Ultrasound-Guided and Landmark-Based Superficial Cervical Plexus Block

2010· article· en· W1997754684 on OpenAlexaff
De Q.H. Tran, Shubada Dugani, Roderick J. Finlayson

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsMedicineUltrasoundNerve blockRandomized controlled trialAnesthesiaLandmarkAnatomical landmarkPlexusCervical plexusSurgeryUltrasonographyRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: This prospective, randomized, observer-blinded study compared ultrasound guidance and the conventional landmark-based technique for superficial cervical plexus blockade. METHODS: Forty patients were randomly allocated to receive a block of the superficial cervical plexus using ultrasound guidance (n = 20) or the traditional landmark-based technique (n = 20). The main outcome, success, was defined as the absence of cold sensation for all 4 branches of the superficial cervical plexus at 15 mins. A blinded observer recorded success rate, onset time, block-related pain scores, and the incidence of complications. Performance time and the number of needle passes were also recorded during the performance of the block. Total anesthesia-related time was defined as the sum of performance and onset times. RESULTS: Success rate (80%-85%) was similar between the 2 groups. Performance time was slightly longer with ultrasonography (119 versus 61 sec, P < 0.001); however, no differences in onset and total anesthesia-related times were found. There were also no differences in the number of passes and procedural discomfort. CONCLUSIONS: Ultrasound guidance does not increase the success rate of superficial cervical plexus block compared with a landmark-based technique. Additional confirmatory trials are required.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.293
Teacher spread0.261 · 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 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

Citations71
Published2010
Admission routes1
Has abstractyes

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