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Record W2396392598 · doi:10.3233/978-1-60750-573-0-44

Using Ultrasound to Guide the Insertion of Pedicle Screws during Scoliosis Surgery

2010· article· en· W2396392598 on OpenAlexaff
Edmond Lou, Chan Zhang, Lawrence H. Le, Douglas L. Hill, Jim Raso, Marc Moreau, James Mahood, Douglas Hedden

Bibliographic record

VenueStudies in health technology and informatics · 2010
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsScoliosisMedicineUltrasoundSurgeryOrthodonticsRadiology

Abstract

fetched live from OpenAlex

Scoliosis surgery involves the insertion of screws and/or hooks into selected vertebrae to secure a pre-bent rod placed along the concave side of the spine. Usually conventional x-rays will be taken before the surgery to plan the alignment and positioning of the pedicle screws. However, reports state that perforation rate range from 6% to 54%. A misalignment of a pedicle screw can potentially cause permanent neurological spinal cord injury or even a life-threatening vascular injury. Because of the importance of positioning and aligning of pedicle screws, we are working on an ultrasound method to guide the insertion of pedicle screws in real time. A pulse-echo immersion experiment was set up to study how well the edges of cortical bone could be detected using a bovine spinous process in-vitro. Two ultrasound frequencies (3.5 MHz and 5.0 MHz) were considered in this study. This preliminary study shows that ultrasound is able to penetrate cortical bone and reflect back from the outer boundary. All interfaces are clearly identified for both frequencies. Strong reflection signals are obtained when the beam is normal to the interface. Derived thickness values from the reflections are comparable with those from micro-CT image. The 5.0 MHz ultrasound frequency provided better resolution than the 3.0 MHz frequency.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.074
GPT teacher head0.424
Teacher spread0.349 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

Explore more

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