Irradiation therapy and chewing simulation: effect on zirconia and human enamel
Bibliographic record
Abstract
PURPOSE: Ionizing radiation therapy (RT) is the main option for head and neck cancer treatment, but it is associated with multiple side effects. This study aimed to evaluate the effect of RT associated with chewing simulation on the surface of human enamel and Yttria-partially stabilized zirconia (Y-TZP). METHODS: Maxillary premolar cusps and Y-TZP slabs were divided in 7 experimental groups: CO: no RT (control); EZ groups had irradiation applied to both, enamel and zirconia samples (simulating restoration prior to RT); E groups had irradiation applied to enamel only (simulating restoration after RT). RT doses were either 30, 50 or 70 Gray (Gy). Enamel cusps were abraded against zirconia slabs in a chewing simulator (CS - one million cycles/ 80 N/ 60 mm/min, 2 mm horizontal path, artificial saliva, 37˚ C). Zirconia hardness was evaluated before CS; zirconia roughness and enamel volume (wear) were evaluated before and after CS. Hardness and wear data were analyzed by one-way Analysis of Variance and Tukey post hoc test. Roughness was analyzed by Repeated Measures test and Bonferroni test (p=0.05). RESULTS: There was no significant effect of enamel or zirconia irradiation on enamel cusp wear (p=0.226), regardless of the irradiation dose used - up to 70 Gy. Irradiation also did not affect Y-TZP surface roughness (p=0.127) and hardness (p=0.964). CONCLUSIONS: RT does not promote significant changes to the surface characteristics of zirconia. Irradiated enamel abraded against zirconia does not show higher wear volume when compared to non-irradiated enamel.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".