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Record W2903051176 · doi:10.1016/j.fdj.2018.11.003

Vital tooth bleaching using different techniques: A clinical evaluation

2018· article· en· W2903051176 on OpenAlexaff
Ana Rita Pinheiro Barcessat, Nalia Gurgel-Juarez, Niklaus Ursus Wetter

Bibliographic record

VenueFuture Dental Journal · 2018
Typearticle
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLightnessHydrogen peroxideCarbamide peroxideDentistryTooth whiteningHueMedicineChemistryOpticsBiochemistry

Abstract

fetched live from OpenAlex

Abstract Objectives This clinical trial aimed to evaluate dental color stabilization after different bleaching techniques. Methods Four dental bleaching techniques were tested in 60 healthy volunteers aged from 25 to 35 years randomly assigned to four groups. Group 1 (G1): conventional in-office bleaching using 35% hydrogen peroxide. Group 2 (G2): in-office application of 3% hydrogen peroxide followed by in-office bleaching using 35% hydrogen peroxide. Group 3 (G3): in-office application of 3% hydrogen peroxide and activation with a light emitting diode (LED) lamp. Group 4 (G4): at-home bleaching using 10% carbamide peroxide. The color of canines and incisors was scored using a digital spectrophotometer to analyze lightness, chroma and hue. Results All groups resulted in shade change. Lightness increased in all groups with no statistical difference among groups 60 days after finishing the treatment regardless of the technique used (p > 0.05). Differences were found in a short-term evaluation between some groups (p   0.05). Analyzing canines, G4 showed higher chroma compared to G1 (p  Conclusions All techniques improved lightness. The addition of 3% hydrogen peroxide to conventional in-office whitening only increased appointment time, but no further benefits were noticed. Clinical relevance This study is important to help clinicians deciding which is the most suitable dental bleaching for each patient in the current high aesthetic demanding world.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.412
Teacher spread0.356 · 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.

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

Citations4
Published2018
Admission routes1
Has abstractyes

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