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Record W3176590917 · doi:10.1136/bmjstel-2021-000868

3D printed ascending aortic simulators with physiological fidelity for surgical simulation

2021· article· en· W3176590917 on OpenAlex

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.

Bibliographic record

VenueBMJ Simulation & Technology Enhanced Learning · 2021
Typearticle
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsStiffnessMaterials scienceUltimate tensile strength3d printedBiomedical engineeringBending stiffnessTensile testingComposite materialMedicine

Abstract

fetched live from OpenAlex

Introduction: Three-dimensional (3D) printed multimaterial ascending aortic simulators were created to evaluate the ability of polyjet technology to replicate the distensibility of human aortic tissue when perfused at physiological pressures. Methods: Simulators were developed by computer-aided design and 3D printed with a Connex3 Objet500 printer. Two geometries were compared (straight tube and idealised aortic aneurysm) with two different material variants (TangoPlus pure elastic and TangoPlus with VeroWhite embedded fibres). Under physiological pressure, β Stiffness Index was calculated comparing stiffness between our simulators and human ascending aortas. The simulators' material properties were verified by tensile testing to measure the stiffness and energy loss of the printed geometries and composition. Results: The simulators' geometry had no effect on measured β Stiffness Index (p>0.05); however, β Stiffness Index increased significantly in both geometries with the addition of embedded fibres (p<0.001). The simulators with rigid embedded fibres were significantly stiffer than average patient values (41.8±17.0, p<0.001); however, exhibited values that overlapped with the top quartile range of human tissue data suggesting embedding fibres can help replicate pathological human aortic tissue. Biaxial tensile testing showed that fiber-embedded models had significantly higher stiffness and energy loss as compared with models with only elastic material for both tubular and aneurysmal geometries (stiffness: p<0.001; energy loss: p<0.001). The geometry of the aortic simulator did not statistically affect the tensile tested stiffness or energy loss (stiffness: p=0.221; energy loss: p=0.713). Conclusion: We developed dynamic ultrasound-compatible aortic simulators capable of reproducing distensibility of real aortas under physiological pressures. Using 3D printed composites, we are able to tune the stiffness of our simulators which allows us to better represent the stiffness variation seen in human tissue. These models are a step towards achieving better simulator fidelity and have the potential to be effective tools for surgical training.

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)0.000
metaresearch head score (Gemma)0.004
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.365
Teacher spread0.336 · 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