Mechanical Properties of the Modified Denture Base Materials and Polymerization Methods: A Systematic Review
Bibliographic record
Abstract
Amidst growing technological advancements, newer denture base materials and polymerization methods have been introduced. During fabrication, certain mechanical properties are vital for the clinical longevity of the denture base. This systematic review aimed to explore the effect of newer denture base materials and/or polymerization methods on the mechanical properties of the denture base. An electronic database search of English peer-reviewed published papers was conducted using related keywords from 1 January 2011, up until 31 December 2021. This systematic review was based on guidelines proposed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The search identified 579 papers. However, the inclusion criteria recognized 22 papers for eligibility. The risk of bias was moderate in all studies except in two where it was observed as low. Heat cure polymethyl methacrylate (PMMA) and compression moulding using a water bath is still a widely used base material and polymerization technique, respectively. However, chemically modified PMMA using monomers, oligomers, copolymers and cross-linking agents may have a promising result. Although chemically modified PMMA resin might enhance the mechanical properties of denture base material, no clear inferences can be drawn about the superiority of any polymerization method other than the conventional compression moulding technique.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".