Effect of ionizing radiation on mechanical properties and translucency of monolithic zirconia
Bibliographic record
Abstract
This study aimed to evaluate the effect of radiation therapy (RT) on mechanical properties and translucency of monolithic zirconia. Yttria- stabilized zirconia (Y-PSZ) materials (14 × 4.0 × 1.5 mm) were divided in four experimental groups (n = 30): High-translucency/control (HT/C), high-translucency/irradiated (HT/I), low-translucency/control (LT/C), low-translucency/irradiated (LT/I). Irradiated specimens were submitted to a single dose irradiation of 70 Gray. Flexural strength (n = 10) (FS-3-point bending test), fatigue limits (n = 15) at 100,000 cycles (FLs-staircase approach), and translucency (n = 5) (TP-dental spectrometer) were analyzed. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to characterize the materials. FS and TP data were analyzed by one-way Analysis of Variance (ANOVA) and Tukey HSD. FLs were analyzed using Dixon and Mood method, and Kaplan-Meier survival analysis. RT affected FS of LT zirconia (p = .032) but not of HT zirconia (p = .86). FLs and TP of both materials were not affected by RT (p > .05). Higher cubic content after RT was observed. In conclusion, RT may affect flexural strength and crystalline content of zirconia-based materials, but this effect was not observed under fatigue. Translucency of Y-PSZ restorations is also not affected by RT.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".