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Record W1744237272 · doi:10.1111/aas.12502

The ultrasound‐assisted paraspinous approach to lumbar neuraxial blockade: a simplified technique in patients with difficult anatomy

2015· article· en· W1744237272 on OpenAlexaff
Ki Jinn Chin, Anahi Perlas, Vincent Chan

Bibliographic record

VenueActa Anaesthesiologica Scandinavica · 2015
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineNeuraxial blockadeUltrasoundLumbarRadiologyEpidural spaceBlockadeFluoroscopySurgery

Abstract

fetched live from OpenAlex

Pre-procedural ultrasound imaging of the spine to identify the interspinous and interlaminar space has been shown to facilitate subsequent performance of lumbar neuraxial blockade. However, adequate visualization of the vertebral canal can be challenging for less-experienced operators, and particularly in subjects with difficult anatomy. In this case report, we describe a simplified technique of ultrasound-assisted neuraxial blockade that addresses these limitations and may thus be a useful fallback option. A pre-procedural scan is performed in which the main ultrasonographic landmarks to be identified are the neuraxial midline and the spinous processes, rather than the posterior and anterior complexes of the vertebral canal. Another key difference is the use of a paraspinous (or paramedian) needle approach rather than a midline approach that is advantageous where the interspinous spaces are narrowed by disease or suboptimal patient positioning. The anatomical basis and technical performance of this novel ultrasound-assisted paraspinous approach are presented in detail.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.854

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)0.0000.000

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 head0.019
GPT teacher head0.251
Teacher spread0.233 · 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

Citations25
Published2015
Admission routes1
Has abstractyes

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