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Record W2091752694 · doi:10.1097/aap.0b013e3181c75a76

A Low-Cost Ultrasound Phantom of the Lumbosacral Spine

2010· article· en· W2091752694 on OpenAlexaff
Geoff Bellingham, Philip Peng

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsImaging phantomMedicineLumbosacral jointGelatinUltrasoundBiomedical engineeringSoft tissueRadiologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: This report describes the production of a low-cost ultrasound phantom of the lumbosacral spine. The phantom should be a very useful tool to teach the basic skills for ultrasound-guided procedures of the lumbosacral spine. METHODS: A lumbosacral spine model is secured to the bottom of a microwave-safe container and is immersed in a concentrated gelatin solution. After the gelatin hardens, the model can be used for scanning practice as well as needle placement. The phantom can be recovered after use by melting the gelatin in a microwave to "erase" any needle track marks. RESULTS: A transparent and durable gelatin block is produced. This allows trainees to have direct visual access to the lumbosacral spine model to correlate with the ultrasound images as well as to confirm proper needle placement. Disadvantages of the model include lack of simulated soft tissue structures and an absence of simulated haptic feedback during needle placement. Metamucil can be added to the gelatin to simulate the appearance of soft tissue, although this increases the opacity and thus decreases the visual access of the gelatin. CONCLUSIONS: This teaching tool can provide trainees with an opportunity to familiarize themselves with sonoanatomy of the lumbosacral spine in addition to practicing probe handling techniques and needle placement.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.285
Teacher spread0.262 · 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.

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

Citations46
Published2010
Admission routes1
Has abstractyes

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