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Record W2902938957 · doi:10.1155/2018/7918781

Healing of Oral Lichenoid Lesions following Replacement of Dental Amalgam Restorations with Feldspathic Ceramic Inlay-Onlay Restorations: Clinical Results of a Follow-Up Period Varied from Three Months up to Five Years

2018· article· en· W2902938957 on OpenAlexaff
Burçin Karataşlı, Özgür Mete, Mehmet Ali Erdem, Abdülkadir Burak Çankaya

Bibliographic record

VenueBioMed Research International · 2018
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of TorontoCanada Research ChairsUniversity Health Network
FundersIstanbul Teknik Üniversitesi
KeywordsAmalgam (chemistry)InlayDentistryMedicine

Abstract

fetched live from OpenAlex

Objective . Previous studies have shown the effect of amalgam removal on the healing of oral lichenoid lesions (OLLs); however, no specific replacement materials have been suggested. The present series evaluated long-term results following the complete replacement of amalgam restorations with feldspathic ceramic inlay-onlay restorations for a group of patients with OLLs whose lesions were suspected to be related to amalgam restorations. Materials and Methods . Twenty-four patients who had OLLs suspected to be related to their amalgam restorations were initially recruited. The patients underwent patch tests for a series of dental materials, in addition to clinical and histopathological examination. Sixteen (67%) of the 24 patients had their amalgam replaced with feldspathic ceramic inlay-onlay restorations and were examined within a follow-up period of 3 months to 5 years. Results . After 3 months of clinical follow-up, complete healing (63%) was noted in all patients with OLLs whose lesions were in only close contact with their amalgam restorations. Healing was significantly related to the combination of lesions with close contact with the amalgam restoration and a diagnosis of OLL (x 2 test, P=0.02). Conclusion . Feldspathic ceramic can be safely used as a replacement material for patients with OLLs to diminish adverse reactions to amalgam restorations.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.106
GPT teacher head0.426
Teacher spread0.320 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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