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Record W1549366199 · doi:10.1111/aej.12027

Irrigation dynamics associated with positive pressure, apical negative pressure and passive ultrasonic irrigations: A computational fluid dynamics analysis

2013· article· en· W1549366199 on OpenAlexaff
José Enrique Chen, Babak Nurbakhsh, Gillian Layton, Markus Bussmann, Anil Kishen

Bibliographic record

VenueAustralian Endodontic Journal · 2013
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsUniversity of Toronto
FundersAnhui University of Science and Technology
KeywordsRoot canalIrrigationShear stressMaterials scienceStress (linguistics)Ultrasonic sensorGeotechnical engineeringDentistryBiomedical engineeringMedicineComposite materialGeologyBiologyAgronomy

Abstract

fetched live from OpenAlex

Complexities in root canal anatomy and surface adherent biofilm structures remain as challenges in endodontic disinfection. The ability of an irrigant to penetrate into the apical region of a canal, along with its interaction with the root canal walls, will aid in endodontic disinfection. The aim of this study was to qualitatively examine the irrigation dynamics of syringe irrigation with different needle tip designs (open-ended and closed-ended), apical negative pressure irrigation with the EndoVac® system, and passive ultrasonic-assisted irrigation, using a computational fluid dynamics model. Syringe-based irrigation with a side-vented needle showed a higher wall shear stress than the open-ended but was localised to a small region of the canal wall. The apical negative pressure mode of irrigation generated the lowest wall shear stress, while the passive-ultrasonic irrigation group showed the highest wall shear stress along with the greatest magnitude of velocity.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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 designSimulation or modeling
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

Citations83
Published2013
Admission routes1
Has abstractyes

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Same venueAustralian Endodontic JournalSame topicEndodontics and Root Canal TreatmentsFrench-language works237,207