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Record W4253098100 · doi:10.1097/aap.0b013e3181aea16f

Ultrasound-Guided Interventional Procedures in Pain Medicine: A Review of Anatomy, Sonoanatomy, and Procedures

2009· review· en· W4253098100 on OpenAlexaff
Philip Peng, Samer Narouze

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2009
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineUltrasoundMagnetic resonance imagingRadiologyInterventional pain managementMedical physicsPain managementMedical imagingPhysical therapy

Abstract

fetched live from OpenAlex

Application of ultrasound in pain medicine is a rapidly growing medical field in interventional pain management. Ultrasound provides direct visualization of various soft tissues and real-time needle advancement and avoids exposing both the health care provider and the patient to the risks of radiation. The machine itself is more affordable than a fluoroscope, computed tomography scan, or magnetic resonance imaging machine. In the present review, we discuss the challenges and limitations of ultrasound-guided procedures for pain management, anatomy, and sonoanatomy of selected pain management procedures and the literature on those selected procedures.

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.043
GPT teacher head0.350
Teacher spread0.307 · 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
GenreReview

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

Citations147
Published2009
Admission routes1
Has abstractyes

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