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Record W2965395755 · doi:10.4012/dmj.2018-286

Incorporation of a hydrophilic amide monomer into a one-step self-etch adhesive to increase dentin bond strength: Effect of application time

2019· article· en· W2965395755 on OpenAlexaff
Yusuke Kuno, Keiichi Hosaka, Masatoshi Nakajima, Masaomi Ikeda, Celso Afonso Klein, Richard M. FOXTON, Junji Tagami

Bibliographic record

VenueDental Materials Journal · 2019
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsSt. Thomas Hospital
FundersMinistry of Education, Science and Technology
KeywordsMaterials scienceMonomerDentinAdhesiveBond strengthAmidePolymerizationUltimate tensile strengthMethacrylateComposite materialPolymer chemistryChemical engineeringPolymerOrganic chemistryChemistryLayer (electronics)

Abstract

fetched live from OpenAlex

The purpose was to evaluate the effect of a hydrophilic amide monomer on μTBS of one-step adhesive to dentin at different application times. Clearfil Universal Bond Quick (UBQ), experimental adhesive (UBQexp; same compositions as UBQ but hydrophilic amide monomer was replaced with 2-hydroxyethyl methacrylate), Clearfil SE ONE and Clearfil SE Bond were applied to midcoronal dentin prepared with 600-grit SiC at different application time (0, 10, 20 and 40 s). Water sorption (Wsp) and the ultimate tensile strength (UTS) of polymerized adhesives were also measured. UBQ showed significantly lower Wsp and higher UTS than UBQexp. At each application time, UBQ exhibited significantly higher μTBSs than UBQexp. UBQ showed the highest μTBS at 0 s application time among all the adhesives. When the application time was prolonged from 0 s to 10 s, only UBQ showed no significant difference. The hydrophilic amide monomer increased μTBS with reduction in Wsp and increase in the UTS and may allow a shortened application time.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.233
Teacher spread0.230 · 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 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

Citations45
Published2019
Admission routes1
Has abstractyes

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