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Record W4413894246 · doi:10.1080/10255842.2025.2552437

Finite element modeling of pedicle screw fixation considering patient-specific bone density

2025· article· en· W4413894246 on OpenAlex
Marie-Hélène Beauséjour, Carolina Solórzano Barrera, Ningxin Qiao, Isabelle Villemure, Carl‐Éric Aubin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2025
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsPolytechnique MontréalCentre Hospitalier Universitaire Sainte-JustineÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFinite element methodFixation (population genetics)OrthodonticsBone densityMedicineComputer scienceStructural engineeringEngineeringOsteoporosisPathology

Abstract

fetched live from OpenAlex

Surgical instrumentation and fusion are necessary in severe cases of spinal deformity. In patients with reduced bone quality, pedicle screw fixation remains challenging due to possible loosening or pullout. The objective was to develop and validate a comprehensive finite element model of pedicle screw fixation considering patient-specific bone density. A bi-planar multi-energy X-ray derived algorithm personalized vertebral bone mechanical properties. It was tested against a reference FEM without patient-specific density (trabecular bone modeled as homogeneous), to assess biomechanical performance. Screw dimensional specifications and trajectory were parametrically modeled, and fixation performance was tested under axial pull-out loads.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.919
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.027
GPT teacher head0.331
Teacher spread0.305 · 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