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Record W2278786851

Direct Composite Resin Veneer Technique: A Clinical Case Report of Management of Misaligned Dentition

2015· article· en· W2278786851 on OpenAlexaff
Zeeshan Sheikh, Nida Zahra Ghazali, Amber Sheikh

Bibliographic record

VenueInternational Dental Journal of Student Research · 2015
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVeneerDentitionDentistryAnterior teethOrthodonticsComputer scienceMedicine
DOInot available

Abstract

fetched live from OpenAlex

The demand for esthetically pleasing restorations in clinical dentistry is ever growing. There are a variety of procedures and material options to choose from. This choice is based upon the wants and desires of the patient. It is very challenging for the dentist to satisfy the needs while at the same time keeping within the budget of the patient. As dentists, it is required of us to develop the skill sets for providing esthetically pleasing results without compromising the biological and functional principles of natural dentition. There is usually no one procedure or material indicated for all situations and providing the patient with multiple options is the key. Veneers are well suited for esthetic and conservative improvement of anterior and posterior teeth. Laboratory fabricated porcelain or composite resin veneers present optimal esthetics and durability. Direct composite veneers provide an additional viable option to the clinician to use where porcelain veneers cannot be used of afforded. Mastering the art of direct composite resin veneering is not easy and is highly technique sensitive. However, when performed for the appropriate case it allows for artistic expression and very impressive clinical results. Here we present a clinical case where a young patient who could not afford extensive dental restorative or orthodontic treatment was treated with direct composite resin veneering to correct misaligned anterior maxillary teeth. The results obtained were both esthetically and functionally acceptable and allowed for the patient to have a smile she otherwise could not attain.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0050.003
Scholarly communication0.0020.003
Open science0.0030.003
Research integrity0.0100.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.193
GPT teacher head0.527
Teacher spread0.334 · 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 designCase report
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

Citations1
Published2015
Admission routes1
Has abstractyes

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