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Record W2043933205 · doi:10.1002/jcu.20588

The use of ultrasound to facilitate spinal anesthesia in a patient with previous lumbar laminectomy and fusion: A case report

2009· article· en· W2043933205 on OpenAlexaff
Ki Jinn Chin, Alan Macfarlane, Vincent Chan, Richard Brull

Bibliographic record

VenueJournal of Clinical Ultrasound · 2009
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineLaminectomyLumbarSurgerySpinal anesthesiaUltrasoundAnesthesiaRegional anesthesiaRadiologySpinal cord

Abstract

fetched live from OpenAlex

We describe a case of ultrasound (US)-facilitated spinal anesthesia in a patient with a prior lumbar laminectomy and spinal fusion who presented for total knee arthroplasty. Traditional, landmark-guided spinal anesthesia had previously failed. Although pre-procedural US identified a soft-tissue window at L3/4, a 25G pencilpoint needle encountered resistance. Reassured from US imaging that this was not bone, we used a 22G cutting tip needle successfully. We believe spinal anesthesia would not have been possible in this patient without US, adding to the evidence that US-facilitated neuraxial anesthesia is useful, particularly in technically difficult, if not 'impossible,' cases.

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)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0050.003
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0120.006
Insufficient payload (model declined to judge)0.0020.001

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 head0.079
GPT teacher head0.342
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations38
Published2009
Admission routes1
Has abstractyes

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