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Record W2590568940

MONITORING OF CEMENT DISTRIBUTION IN VERTEBRAL BODIES DURING VERTEBROPLASTY

2018· article· en· W2590568940 on OpenAlexaboutno aff
Ivan Zderic, Markus Windolf, Boyko Gueorguiev, Vincent A. Stadelmann

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
Fundersnot available
KeywordsCadaveric spasmBone cementCementPercutaneous vertebroplastyMedicineCannulaVertebraVertebral bodyBiomedical engineeringSurgeryMaterials scienceComposite material
DOInot available

Abstract

fetched live from OpenAlex

Summary Time-lapsed CT offers new opportunities to predict the risk of cement leakage and to evaluate the mechanical effects on a vertebral body by monitoring each incremental injection step in an in-vitro vertebroplasty procedure. Introduction Vertebroplasty has been shown to reinforce weak vertebral bodies and to prophylactically reduce fracture risks. However, bone cement leakage is a major vertebroplasty related problem which can cause severe complications. Leakage risk can be minimised by injecting less cement into the vertebral body, inevitably compromising the mechanical properties of the augmented bone, as a proper endplate-to-endplate connection of the injected cement is needed to obtain a mechanical benefit. Thus the cement flow in a vertebroplasty procedure requires a better understanding. This study aimed at developing a method to monitor the cement flow in a vertebral body and its mechanical effect. Materials and Methods Eight fresh frozen human cadaveric vertebrae were prepared for augmentation by performing a bitrans- or bipedicular approach. Following they were XTremeCT-scanned (Scanco, Switzerland) at a nominal resolution of 82µm. A custom made setup enabled to fix the vertebrae in the CT bore (Siemens Emotion6) centrically. Bone cement (Vertecem V+, Synthes GmbH, Switzerland) was injected monopedicularly via a syringe driver (Harvard Apparatus, USA). Injection forces were recorded through a load cell (Type 9211, Kistler Instrumente AG, Switzerland) placed on the driver. Either a custom PEEK cannula or a trocar was inserted into each pedicle of a vertebra to allow artifact-free CT scanning. After each milliliter of injection a CT scan of the vertebra was performed at a nominal resolution of 0.63mm. Subsequently, the CT images were resampled to the original XTremeCT image and the cement cloud was segmented. The image data were then further processed for micro finite element (microFE) modeling (FAIM, Numerics88, Canada). The models were then solved for axial stiffness and Von Mises Stress (VMS) distribution. Finally, the vertebrae underwent a biomechanical quasistatic axial compression test (Mini Bionix II 858, MTS Systems Corp., USA). Results Endplate-to-endplate connection of the cement was reached in 4 vertebrae. The average volume needed to reach the connection was 5.0±1.2 ml. Cement leakage occurred in all vertebrae, whereby in 4 cases the cement leaked into the spine channel. Each successive cement injection step was characterised with an increase of peak injection forces (16.5±12.7N at 1ml to 70.82±21.14N at 6ml). With respect to axial stiffness the mechanical tests and the microFE models correlated well (R 2 = 0.778). Analyzing the top 100 VMS an elevated stress concentration between the endplate and the cement was observed unless the endplate was in direct contact with the cement. Conclusion Cement flow can be monitored precisely at each injection step using the time-lapsed CT approach. Combined with microFE modeling the mechanical properties of the augmented bone can be evaluated for different incremental cement volumes injected. Our results suggest augmenting the bone until an endplate-to-endplate connection is established as otherwise partial filling would increase the risk of failure in the trabecular bone structure. This is in close agreement to other studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.243
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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