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Record W4386933564 · doi:10.3390/dj11090225

Retrospective Clinical Evaluation of RMGIC/GIC Class V Restorations

2023· article· en· W4386933564 on OpenAlexaff
Maria Jacinta Moraes Coelho Santos, Lucy Leon, Imad Siddique, Sheila Butler

Bibliographic record

VenueDentistry Journal · 2023
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsWestern University
Fundersnot available
KeywordsGlass ionomer cementDentistryMedicineStatistical significanceRetrospective cohort studyExact testInternal medicine

Abstract

fetched live from OpenAlex

The aim of this retrospective study was to evaluate the clinical performance of glass-ionomer cement (GIC) and resin-modified glass-ionomer cement (RMGIC) materials in Class V carious cervical lesions restored by dental students. Ninety-six (96) restorations performed with either GIC (Fuji IX) (n = 39) or RMGIC (Fuji II LC) (n = 57) were evaluated using the modified USPHS criteria by two independent investigators at two follow-up evaluations (two years apart). The Fisher statistical test was used to compare USPHS criteria and examine significant differences, with a significance level set at p < 0.05. The Kaplan-Meier algorithm was used to calculate the survival probability. The overall success rate of Class V restorations was 72.9% at the second follow-up evaluation, with restorations ranging in age from 2.5 to 3.5 years. The RMGIC (Fuji II LC) restorations exhibited a significantly higher overall success rate compared to the GIC (Fuji IX) restorations (p = 0.0104). Significant differences were observed in retention (p = 0.0034) and color match (p = 0.0023).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.127
GPT teacher head0.435
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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