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Record W2141137475 · doi:10.4317/medoral.15.e79

Study of the interocclusal distortion in impressions taken with different types of closed-mouth trays and two types of impression materials

2009· article· en· W2141137475 on OpenAlexaboutno aff
JF. Manes-Ferrer, EJ Selva-Otaolaurruchi, C Parra-Arenos, I Selfa-Bas

Bibliographic record

VenueMedicina oral, patología oral y cirugía bucal · 2009
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsImpressionTrayPoint (geometry)Distortion (music)DentistryOrthodonticsMathematicsComputer scienceMedicineEngineeringMechanical engineeringGeometry

Abstract

fetched live from OpenAlex

The aim of this study was to compare different types of impression trays for the closed-mouth impression technique, using two different types of impression material. For this study, five different types of impression trays were used with two different types of impression materials, one of addition silicone and the other of polyether. We designed a model used for taking the impressions and for measuring interocclusal distortion. The results obtained show that the impression trays COE (GC (R) GC America INC. Alsip) and Premier (Premier (R), Premier Dental Products Co. Canada) show a lesser degree of interocclusal distortion when taking closed-mouth impressions. In terms of impression materials, the polyether was the one that produced the best results. From a clinical point of view, our study shows that the use of these types of trays is absolutely recommendable when used according to the clinical indications for which they have been designed; that said, we must not fail to consider that selecting the proper type of tray is also important.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.374
Teacher spread0.339 · 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 designBench or experimental
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

Citations0
Published2009
Admission routes1
Has abstractyes

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