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Record W1627426086

Coronal micro leakage of lingual access restoration in anterior teeth - an in-vitro study

2012· article· en· W1627426086 on OpenAlexaff
Aditya Mitra, Swati Mitra, Rana K Verghese, Ashistaru Saha

Bibliographic record

VenueInternational journal of dental clinics · 2012
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsGlass ionomer cementDentistryZinc oxide eugenolZinc phosphateMedicineAnterior teethCoronal planeGutta-perchaMolarMaterials scienceZincDental restorationRoot canal
DOInot available

Abstract

fetched live from OpenAlex

Aims: To evaluate in vitro the extent of coronal microleakage in endodontically treated anterior teeth using combination i.e., glass ionomer and composite resin materials with zinc oxide eugenol, zinc phosphate and zinc polycarboxylate reinforced with tannic acid and zinc fluoride. Materials and Method: An in vitro study was conducted comparing two different permanent restorative materials and three different base materials to restore lingual access cavity of 92 endodontically treated permanent maxillary anterior teeth. Linear dye penetration was measured along the tooth restoration interface in 92 sectioned teeth. The positive control tooth exhibited leakage involving the total length of gutta percha. The negative control tooth did not demonstrate any leakage. Mean length of restorative materials, base materials, mean leakage of restorative materials and base materials as well as mean length of gutta percha were evaluated by analysis of variance. Results: The positive control tooth exhibited leakage involving the total length of gutta percha. The negative control tooth did not demonstrate any leakage. The results showed least penetration of silver nitrate dye in teeth restored with composite resin over glass ionomer base. Conclusion: In conclusion maximum linear dye penetration was measured in teeth restored with glass ionomer restoration over zinc phosphate cement base.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.086
GPT teacher head0.489
Teacher spread0.403 · 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 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

Citations0
Published2012
Admission routes1
Has abstractyes

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