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Record W2112837935 · doi:10.1177/0954411912445729

Finite-element study of cerebrospinal fluid in mitigating closed head injuries

2012· article· en· W2112837935 on OpenAlexaff
Yunhua Luo, Zhaoxia Li, Hongxi Chen

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInviscid flowCerebrospinal fluidFinite element methodIntracranial pressureHead (geology)Fluid–structure interactionFluid pressureInterstitial fluidFluid dynamicsSkullMechanicsMedicineMaterials scienceGeologyPhysicsStructural engineeringEngineeringSurgeryPathology

Abstract

fetched live from OpenAlex

The mechanism of cerebrospinal fluid in mitigating closed head injuries caused by mild impacts was investigated by finite-element modeling. Three biomechanical models were constructed. In these models, cerebrospinal fluid was considered as a soft solid material, an inviscid fluid without intracranial pressure, and an inviscid fluid with normal intracranial pressure, respectively, while other conditions such as the finite-element mesh, the impact, and the boundary conditions were kept the same. The recently developed nearest nodes finite-element method was adopted to deal with large deformations in brain tissue. Results obtained from the numerical studies showed that cerebrospinal fluid was able to remarkably reduce the maximum peak strains, especially the shear strains induced by impacts and transmitted to the brain. Cerebrospinal fluid with intracranial pressure was able to further buffer relative oscillations between the skull and the brain.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.290
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations23
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in MedicineSame topicAutomotive and Human Injury BiomechanicsFrench-language works237,207