On the behaviour of zirconia-based dental materials: A review
Bibliographic record
Abstract
Zirconia-based dental materials are extensively used in clinical practice due to their tooth-like appearance, biofunctionality, biocompatibility, and affordability. However, premature clinical failures of veneering porcelains raise a concern about their integrity. Extensive studies have been performed over a decade to resolve this issue, but it is challenging to reference all information effectively. A single source identifying the significance of potential parameters on material performance has not previously been available. An evidence-based meta-narrative review technique was used to review the characteristic parameters that can affect the overall behaviour of zirconia-based materials. Keywords were chosen to assess manuscripts based on scientific coherence with this paper's research objective. Online keyword searches were carried out on ScienceDirect, PubMed, and SAGE databases for relevant published manuscripts from year 1985-2020.261 out of 3170 identified manuscripts were included. A total of 10 parameters were identified and classified into the material, manufacturing, and geometric aspects. The effect of every parameter was reviewed on the performance of the material. A discrepancy in findings was observed and is attributed to the fact that there is no standard methodology. This review acts as a single source that summarizes various parameters' contribution to zirconia-based dental materials' performance. This review facilitates manufacturing improvements by accounting for every parameter's effect on overall performance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".