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Ultrasound-Guided Infraclavicular Versus Supraclavicular Block

2005· article· en· W2010455751 on OpenAlexaff
Genevi ve Arcand, Stephan Williams, Philippe Chouinard, Daniel Boudreault, Patrick G. Harris, Monique Ruel, Fran ois Girard

Bibliographic record

VenueAnesthesia & Analgesia · 2005
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsHôpital Notre-Dame
Fundersnot available
KeywordsMedicineUltrasoundBlock (permutation group theory)UltrasonographyRadiologyMathematics

Abstract

fetched live from OpenAlex

In this prospective study we compared ultrasound-guided (USG) infraclavicular and supraclavicular blocks for performance time and quality of block. We hypothesized that the infraclavicular approach would result in shorter performance times with a quality of block similar to that of the supraclavicular approach. Eighty patients were randomized into two equal groups: Group I (infraclavicular) and Group S (supraclavicular). All blocks were performed using ultrasound visualization with a 7.5-MHz linear probe and neurostimulation. The anesthetic mixture consisted of 0.5 mL/kg of bupivacaine 0.5% and lidocaine hydrocarbonate 2% (1:3 vol.) with epinephrine 1:200,000. Sensory block, motor block, and supplementation rates were evaluated for the musculocutaneous, median, radial, and ulnar nerves. Surgical anesthesia without supplementation was achieved in 80% of patients in group I compared with 87% in Group S (P = 0.39). Supplementation rates were significantly different only for the radial territory: 18% in Group I versus 0% in group S (P = 0.006). Block performance times were not different between groups (4.0 min in Group I versus 4.65 min in Group S; P = 0.43). Technique-related pain scores were not different between groups (I: 2.0; S: 2.0; P = 1.00). We conclude that USG infraclavicular block is at least as rapidly executed as USG supraclavicular block and produces a similar degree of surgical anesthesia without supplementation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.002

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 head0.281
Teacher spread0.263 · 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
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

Citations144
Published2005
Admission routes1
Has abstractyes

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