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Record W4327949478 · doi:10.3390/medicina59030603

Minimally Invasive Laminate Veneer Therapy for Maxillary Central Incisors

2023· article· en· W4327949478 on OpenAlexaff
Gerardo Guzman‐Perez, Carlos A. Jurado, Francisco X. Azpiazu‐Flores, Kelvin I. Afrashtehfar, Akimasa Tsujimoto

Bibliographic record

VenueMedicina · 2023
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaxillary central incisorVeneerDentistryMedicineReduction (mathematics)Orthodontics

Abstract

fetched live from OpenAlex

Minimally invasive dentistry is a considered process that requires the clinician to be prepared with the ideal sequence and the tools needed. This report describes a well-planned ultraconservative approach using only two ceramic laminate veneers for the maxillary central incisors to significantly improve the patient's overall smile. A 30-year-old female presented with the chief complaints of having diastemas between the central and lateral incisors as well as incisal wear. Diagnostic wax-up and mock-up were performed, and the patient approved the minimally invasive treatment with veneers only for central incisors. A reduction guide aided the conservative tooth preparations, and hand-crafted feldspathic veneers were bonded under total isolation with a rubber dam. The two final conservative veneers significantly improved the smile and fulfilled the patient's expectations. Following proper planning and sequencing, predictable outcomes were obtained and fulfilled the patient's esthetic demands. Minimally invasive restorative dentistry with only two single veneers can impact the entire smile frame. Overtreatment in the esthetic zone is unnecessary to meet a patient's esthetic expectations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.301
Teacher spread0.269 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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