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Record W4402340456 · doi:10.18502/fid.v21i33.16436

In Vitro Efficacy of Tricalcium Phosphate and Casein Phosphopeptide Amorphous Calcium Phosphate Fluoride for Remineralization of Enamel White Spot Lesions

2024· article· en· W4402340456 on OpenAlexaff
Alireza Haerian, S Yasaei, Elaheh Rafiei, Seyed Vahid Malek Hosseini, Negin Karimi

Bibliographic record

VenueFrontiers in Dentistry · 2024
Typearticle
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsUniversity of British Columbia
FundersShahid Sadoughi University of Medical Sciences
KeywordsRemineralisationEnamel paintCaseinAmorphous calcium phosphateTooth RemineralizationChemistryDentistryPhosphopeptideFluorideCalciumNuclear chemistryPhosphateMaterials scienceMedicineFood scienceMetallurgyBiochemistryInorganic chemistry

Abstract

fetched live from OpenAlex

Objectives: The main purpose of this study was to compare the remineralizing effects of casein phosphopeptide amorphous calcium phosphate fluoride (CPP-ACPF) and tricalcium phosphate (TCP) on artificially induced enamel white spot lesions (WSLs). Materials and Methods: In this in vitro study, 45 sound extracted premolars were immersed in a demineralizing solution (pH=4.5) for 96 hours, and were randomly divided into 3 groups of TCP, MI Paste Plus, and control. They were exposed to the remineralizing agents for 5 minutes once a day for 30 days. After mounting the teeth in resin blocks and polishing, they underwent a microhardness test at 3 different depths from the enamel surface. Data were analyzed by Prism software, two-way ANOVA, and Tukey’s test (α=0.05). Results: The volume percentage of mineral content (VPM) was significantly different among the three groups at 30-, 60- and 90µm depths (P<0.0001). At 30µm depth, CPP-ACPF was significantly more effective than TCP (P<0.0001). At 60- and 90µm depths, there was no significant difference between CPP-ACPF and TCP (P>0.05). Conclusion: Both CPP-ACPF and TCP had significant efficacy for remineralization of artificially induced enamel WSLs under in vitro conditions.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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