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Record W4306773725 · doi:10.1177/21925682221134913

Biomechanical Comparison of Subsidence Between Patient-Specific and Non-Patient-Specific Lumbar Interbody Fusion Cages

2022· article· en· W4306773725 on OpenAlexaff
Renan Fernandes, Aaron Gee, Andrew Kanawati, Fawaz Siddiqi, Parham Rasoulinejad, Radovan Zdero, Christopher S. Bailey

Bibliographic record

VenueGlobal Spine Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsLondon Health Sciences CentreLawson Health Research InstituteWestern University
Fundersnot available
KeywordsCadaveric spasmMedicineCageStiffnessLumbarOrthodonticsBiomedical engineeringSurgeryMaterials scienceComposite materialStructural engineering

Abstract

fetched live from OpenAlex

Study Design Biomechanical study. Objectives Several strategies to improve the surface of contact between an interbody device and the endplate have been employed to attenuate the risk of cage subsidence. 3D-printed patient-specific cages have been presented as a promising alternative to help mitigate that risk, but there is a lack of biomechanical evidence supporting their use. We aim to evaluate the biomechanical performance of 3D printed patient-specific lumbar interbody fusion cages in relation to commercial cages in preventing subsidence. Methods A cadaveric model is used to investigate the possible advantage of 3D printed patient-specific cages matching the endplate contour using CT-scan imaging in preventing subsidence in relation to commercially available cages (Medtronic Fuse and Capstone). Peak failure force and stiffness were analyzed outcomes for both comparison groups. Results PS cages resulted in significantly higher construct stiffness when compared to both commercial cages tested (>59%). PS cage peak failure force was 64% higher when compared to Fuse cage ( P < .001) and 18% higher when compared to Capstone cage ( P = .086). Conclusions Patient-specific cages required higher compression forces to produce failure and increased the cage-endplate construct' stiffness, decreasing subsidence risk.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.764
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.315
Teacher spread0.286 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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