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Record W3042290685 · doi:10.20396/revpibic2720192330

Comparative study of methods to evaluate depth of cure of restorative composites

2019· article· en· W3042290685 on OpenAlexaff
Beatriz de Cássia Romano, Marcelo Giannini, Jorge Soto‐Montero, Beatriz de Mendonça, Richard Bengt Price

Bibliographic record

VenueResumos do... · 2019
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsDalhousie University
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsMaterials scienceAcetoneComposite materialCuring (chemistry)SolventResin compositeDissolutionComposite numberOrganic solventChemistryChemical engineering

Abstract

fetched live from OpenAlex

The objective of this study was to compare two methods to evaluate the depth of cure (DOC) of restorative composites and if the extension of the light-activation time would increase the DOC of composites.Two bulk-fill composites (Tetric Evoceram Bulk Fill, Ivoclar Vivadent and Filtek One Bulk Fill, 3M Oral Care) and two conventional composites (Tetric N-Ceram, Ivoclar Vivadent e Filtek Z350 XT, 3M Oral Care) were tested. Cylindrical samples were made by inserting the composites into a hole of matriz with 5 mm internal diameter and 13 mm depth, and light-activated with light curing unit from the same manufacturers of each composite, using the manufacturer recommendation time (MRT), or for double the time (DOT). Two methods compared were: 1- ISO 4049 test and 2- dissolution with organic solvent (acetone). For the ISO 4049 test, the unpolymerized resin was manually removed with a plastic spatula and the length of the samples measured with a digital caliper to calculate the DOC. In the organic solvent method, the unpolymerized resin was removed manually and samples were immersed inn the dark in acetone for 48 hours for DOC measurement. It was observed that light curing for DOT produced significantly higher DOC than those for MRT. Differences in DOC measurements between ISO and "organic solvent" methods were material dependent.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.331
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.080
GPT teacher head0.449
Teacher spread0.368 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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