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Record W4388764529 · doi:10.1080/10255842.2023.2280764

A novel method for assigning bone material properties to a comprehensive patient-specific pelvic finite element model using biplanar multi-energy radiographs

2023· article· en· W4388764529 on OpenAlex
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 · 2023
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsPolytechnique MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersNatural Sciences and Engineering Research Council of CanadaMedtronic
KeywordsFinite element methodFixation (population genetics)CalibrationRadiographyOrthodonticsPelvisBiomedical engineeringMaterials scienceStructural engineeringComputer scienceSurgeryMedicineEngineeringMathematics

Abstract

fetched live from OpenAlex

The increasing prevalence of adult spinal deformity requires long spino-pelvic instrumentation, but pelvic fixation faces challenges due to distal forces and reduced bone quality. Bi-planar multi-energy X-rays (BMEX) were used to develop a patient-specific finite element model (FEM) for evaluating pelvic fixation. Calibration involved 10 patients, and an 81-year-old female test case was used for FEM customization and pullout simulation validation. Calibration yielded a root mean square error of 74.7 mg/cm3 for HU. The simulation accurately replicated the experimental pullout test with a force of 565 N, highlighting the method’s potential for optimizing biomechanical performance for pelvic fixation.

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 categoriesMeta-epidemiology (narrow)
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 score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
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 head0.345
Teacher spread0.272 · 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