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Dimensional stability and dehydration of a thermoplastic polycarbonate‐based and two PMMA‐based denture resins

2003· article· en· W1735031656 on OpenAlexaff
G. J. Pronych, E.J. Sutow, O Sykora

Bibliographic record

VenueJournal of Oral Rehabilitation · 2003
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPolycarbonateThermoplasticDehydrationMaterials scienceComposite materialPolymer chemistryChemistry

Abstract

fetched live from OpenAlex

This study compared the dimensional stability and dehydration of a thermoplastic polycarbonate denture base resin with two conventional polymethyl methacrylate denture base resins. Maxillary complete dentures were fabricated from the three denture materials and the accuracy of fit along the posterior palatal border of the cast used in processing was measured. Measurements were conducted at five palatal locations immediately after processing and at 7 and 30 days during immersion in water (23 degrees C) and at 7 and 30 days during dehydration (23 degrees C, 65-75% relative humidity). Percentage mass loss during dehydration was determined with an electronic balance. The thermoplastic material was separately compared with each of the conventional resins using a modified Welch two-sample t-test, with a Bonferroni correction for P values. For mean palatal dimensional change, the thermoplastic resin was generally not statistically different from the conventional resins after processing and during immersion (P > or = 0.06), but was generally less than the conventional resins during dehydration (P < or = 0.02). For mean percentage mass loss, the thermoplastic resin consistently showed much smaller, statistically significant values compared with the conventional resins (P < 0.001). It was concluded that the thermoplastic resin should show dimensional changes in service comparable with the conventional resins, but less dimensional change caused by dehydration.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.809
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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

Citations29
Published2003
Admission routes1
Has abstractyes

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