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Beverage Influence on Direct Restorations

2019· article· en· W2961608310 on OpenAlexaboutno aff
Ali A. Razooki Al-Shekhli, Isra'a AA Aubi

Bibliographic record

VenueWorld Journal of Dentistry · 2019
Typearticle
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsDentistryOrthodonticsMedicine

Abstract

fetched live from OpenAlex

Aim:The aim of this study was to evaluate and compare the beverages effect on the microhardness of composite and compomer direct restorative materials in comparison with mineral water.Materials and methods: Two types of direct restorative materials of A3 shade were selected for this study: Composan Bio-esthetic Nano-ceram Composite (PROMEDICA) and Composan compomer (PROMEDICA).Forty specimens were prepared from each restorative material (total number of specimens were 80).Each specimen was prepared by compressing a sufficient amount of material into a mold of 4 mm in diameter and 2 mm in thickness by two glass slides with acetate celluloid strip in between and curing the specimen for 20 seconds from only the top surface by making the curing tip in intimate contact with the acetate celluloid strips covering the composite and compomer surface with LED Woodpacker light curing unit.The top and bottom surfaces were divided into two halves: 1st half was subjected to microhardness testing before immersion, while microhardness testing was performed on the 2nd half after immersion in beverages.PH values were recorded for each beverage solution with pH meter (Mettler Toledo, Canada).Vickers microhardness testing was performed with a microhardness tester (Microhardness tester FM-800, Future-Tech, Japan) at 300 g load and 15 seconds according to ISO 4049 for both top and bottom surfaces by making three indentations and considering the mean microhardness value for each surface to be the Vickers hardness number for that surface.Three types of beverages were used in the study (Coca Cola, orange juice, Red Bull) and a fourth immersion solution of mineral water was used as a control group.The 80 specimens were immersed for 30 days at 37°C.The immersion solutions were replaced in a daily manner.After immersion, the composite and compomer specimens were incubated in distilled water at 37°C for 24 hours before the microhardness testing.Data were statistically analyzed before and after immersion of the 80 specimens using mean, standard deviation, one way ANOVA and t-test at a 5% level of significance.Results: One-way analysis of variance (ANOVA) for VHN composite top, bottom, and compomer top, bottom revealed that there was a statistically significant difference (p ≤ 0.05).t-tests comparing all the groups before and after immersion showed that there were statistically significant differences (p ≤ 0.05) in all groups being tested in this study.pH values for all the solutions were recorded as the followings: pH for Cola was 1.87, orange juice was 2.63, Red Bull was 2.55 and for water was 6.96.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.004

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 head0.013
GPT teacher head0.285
Teacher spread0.272 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations3
Published2019
Admission routes1
Has abstractyes

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