MétaCan
Menu
Back to cohort
Record W1999354905 · doi:10.1016/j.rapm.2004.01.001

Ultrasound imaging for popliteal sciatic nerve block

2004· article· en· W1999354905 on OpenAlexaff
Avinash Sinha

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPopliteal fossaMedicineSciatic nerveUltrasoundNerve blockCommon peroneal nerveAnatomyRadiology

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Ultrasound is a novel method of nerve localization but its use for lower extremity blocks appears limited with only reports for femoral 3-in-1 blocks. We report a case series of popliteal sciatic nerve blocks using ultrasound guidance to illustrate the clinical usefulness of this technology. CASE REPORT: The sciatic nerve was localized in the popliteal fossa by ultrasound imaging in 10 patients using a 4- to 7-MHz probe and the Philips ATL HDI 5000 unit. Ultrasound imaging showed the sciatic nerve anatomy, the point at which it divides, and the spatial relationship between the peroneal and tibial nerves distally. Needle contact with the nerve(s) was further confirmed with nerve stimulation. Circumferential local anesthetic spread within the fascial sheath after injection appears to correlate with rapid onset and completeness of sciatic nerve block. CONCLUSIONS: Our preliminary experience suggests that ultrasound localization of the sciatic nerve in the popliteal fossa is a simple and reliable procedure. It helps guide block needle placement and assess local anesthetic spread pattern at the time of injection.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.268
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations89
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueRegional Anesthesia & Pain MedicineSame topicAnesthesia and Pain ManagementFrench-language works237,207