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Record W2029886297 · doi:10.4012/dmj.2012-031

Effect of smear layer treatment on dentin bond of self-adhesive cements

2012· article· en· W2029886297 on OpenAlex
Keisuke KAMBARA, Masatoshi Nakajima, Keiichi Hosaka, Masahiro Takahashi, Ornnicha Thanatvarakorn, Shizuko Ichinose, Richard M. FOXTON, Junji Tagami

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

VenueDental Materials Journal · 2012
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsSt. Thomas Hospital
FundersMinistry of Education, Science and Technology
KeywordsDentinSmear layerMaterials scienceBond strengthAdhesiveCementSelf adhesiveComposite materialLayer (electronics)DentistryNuclear chemistryChemistry

Abstract

fetched live from OpenAlex

The purpose of this study was to compare the dentin bond strength of three self-adhesive cements with smear layer pretreatments using a calcium-chelating agent (EDTA) and deproteinizing solution (NaOCl) and to evaluate their interfacial characteristics. Smear layer-covered dentin surfaces were pretreated with EDTA for 60 s, NaOCl for 5 and 15 s, or none. Three self-adhesive cements; Clearfil SA luting (Kuraray Medical), Rely X Unicem clicker (3M ESPE) and Breeze (Pentron) were applied to the dentin surfaces. After 24-h water storage, shear bond strengths to dentin were determined. In addition, nanoleakage evaluation at the interface was performed using FE-SEM and EDS. EDTA-pretreatment significantly improved the bond strength of BR (p<0.05) and NaOCl-pretreatment for 15 s significantly improved the bond strength of RX (p<0.05). On the other hand, for SA, both pretreatments significantly decreased bond strength to dentin (p<0.05). Nanoleakage formation was observed in various amounts at the cement-dentin interfaces.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.301
Teacher spread0.285 · 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