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Record W3193522326 · doi:10.4103/1735-3327.324028

Lingual retainer materials

2021· article· en· W3193522326 on OpenAlexaff
Mohsen Nosouhian, Mohamad Monirifard, Fateme Gharibpour, Saeed Sadeghian

Bibliographic record

VenueDental Research Journal · 2021
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of British Columbia Hospital
Fundersnot available
KeywordsSandpaperRetainerMaterials scienceComposite numberComposite materialWear resistanceNanocomposite

Abstract

fetched live from OpenAlex

Background: A bonded fixed retainer is used to stabilize the alignment of the teeth. Different composites have been introduced for this purpose. This study aimed to investigate the wear resistance of flowable nanocomposite in comparison with microhybrid composite in an in vitro situation. Materials and Methods: In this in vitro study, 46 disk-shaped specimens were divided into two groups: Filtek Ultimate flowable composite and Z250 microhybrid composite. The samples were prepared in 8 mm diameter and 3 mm thickness in an aluminum mold and light cured. They were polished with 600 grit sandpaper to achieve a smooth surface. Two-body wear test was accomplished by the pin-on-disk device (under 15 N, 20 rpm for 1 h). Analyzing the weight and thickness of specimens before and after the assay demonstrates the wear resistance. Data were analyzed using the t -test. P ≤ 0.05 was considered statistically significant. Results: The Filtek Ultimate flowable composite shows no significant difference compared to Z250 microhybrid composite in thickness ( P = 0.701) and weight ( P = 0.939) of specimens. Conclusion: Due to wear resistance of both materials, flowable composite can be recommended as an alternative material for bonded fixed retainers.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.093
GPT teacher head0.432
Teacher spread0.339 · 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 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

Citations5
Published2021
Admission routes1
Has abstractyes

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