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Record W2029195285 · doi:10.1177/0022034514553976

Resin-composite Blocks for Dental CAD/CAM Applications

2014· review· en· W2029195285 on OpenAlexaff
N. Dorin Ruse, Michaël Sadoun

Bibliographic record

VenueJournal of Dental Research · 2014
Typereview
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsCADDental ceramicsImpressionMachinabilityMaterials scienceCeramicComputer-aided manufacturingComposite numberDentistryComputer scienceComposite materialEngineering drawingMachiningEngineeringMedicineMetallurgyWorld Wide Web

Abstract

fetched live from OpenAlex

Advances in digital impression technology and manufacturing processes have led to a dramatic paradigm shift in dentistry and to the widespread use of computer-aided design/computer-aided manufacturing (CAD/CAM) in the fabrication of indirect dental restorations. Research and development in materials suitable for CAD/CAM applications are currently the most active field in dental materials. Two classes of materials are used in the production of CAD/CAM restorations: glass-ceramics/ceramics and resin composites. While glass-ceramics/ceramics have overall superior mechanical and esthetic properties, resin-composite materials may offer significant advantages related to their machinability and intra-oral reparability. This review summarizes recent developments in resin-composite materials for CAD/CAM applications, focusing on both commercial and experimental materials.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.010

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 head0.491
Teacher spread0.364 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations492
Published2014
Admission routes1
Has abstractyes

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