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Record W3010095380 · doi:10.1002/jum.15258

Ultrasound‐Guided Anterior Approach to a Sciatic Nerve Block

2020· article· en· W3010095380 on OpenAlexaff
Ha‐Jung Kim, Ki Jinn Chin, Hyungtae Kim, Hwa‐Young Jang, Seong‐Il Bin, Young‐Jin Ro, Won Uk Koh

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineSciatic nerveVisibilityUltrasoundNerve blockAnatomyBlock (permutation group theory)Ultrasound imagingUltrasonographyAnesthesiaSurgeryRadiologyGeometryOptics

Abstract

fetched live from OpenAlex

OBJECTIVES: We aimed to identify the optimal lower limb position for an ultrasound (US)-guided anterior approach to a sciatic nerve block. METHODS: We included 45 patients who met the following criteria: American Society of Anesthesiologists physical status of 1 to 3, age between 18 and 80 years, and scheduled to undergo knee surgery that required a sciatic nerve block. The lower limbs of each patient were placed in the following 4 positions: N, neutral; ER, external rotation of the hip (angle, 45°); ER/F15, ER (angle, 45°) and flexion (angle, 15°) of the hip; and ER/F45, ER (angle, 45°) and F (angle, 45°) of the hip. An investigator acquired US scans of the sciatic nerve in each position, and the visibility score and depth of the sciatic nerve from the skin were analyzed. RESULTS: The visibility scores were significantly higher in positions ER/F15 and ER/F45 than in positions ER and N (P < .0001). However, there was no difference between the visibility scores in positions ER/F15 and ER/F45 (P = .0959). The depth of the sciatic nerve from the skin decreased with ER and an increase in the F angle of the hip (overall P < .0001). CONCLUSIONS: Based on the visibility score and depth from the skin, ER of the hip to 45° with a greater F angle (45° versus 15°) of the hip appears to be the optimal position for an US-guided anterior approach to a sciatic nerve 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 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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.292
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 integrity0.0000.001
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 head0.034
GPT teacher head0.290
Teacher spread0.256 · 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.

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

Citations13
Published2020
Admission routes1
Has abstractyes

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