Wear resistance of <scp>CAD</scp>/<scp>CAM</scp> one‐piece screw‐retained hybrid‐abutment‐crowns made from different restorative materials
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to measure the wear progress of three high performance polymers (HPP) materials as well as that of zirconia after artificial aging (simulated 2.5- and 5-year of clinical service with thermo-mechanical loading) and compare it with the well-documented wear of lithium disilicate. METHODS: Forty implants were used to restore a maxillary first premolar, where the abutment and the crown were manufactured as hybrid-abutment-crown and connected to the implant using a titanium insert. The implants were randomly divided, according to the restorative materials used, into five groups: 3Y-TZP zirconia (Z), lithium disilicate (L), ceramic-reinforced polyetheretherketon (P), nano-hybrid composite resin (C) and polymer-infiltrated ceramic-network (E). All hybrid-abutment-crowns were produced using CAD/CAM technology. A design of a maxillary first premolar was created with an angle of 120° between the buccal and palatal cusps, which were designed as planes. The restorations were adhesively luted onto the titanium inserts, according to the manufacturers' recommendations for each material individually, by means of dual-curing luting resin with the exception of group P, where the blocks were pre-fitted (heat-pressed) with an integrated titanium insert. The suprastructures were assembled onto the implants through titanium screws. The screw channels were sealed with Teflon tape and composite resin filling material that was polished to high-gloss. All specimens underwent 1 200 000 thermo-dynamic loading cycles with 49 N in a dual-axis chewing simulator. Elastomeric impressions were made for all specimens after 600 000 and after 1 200 000 cycles. The corresponding impressions were imaged using a laser scanning microscope and then 3D-analyzed using the software (Geomagic Wrap) to measure the volume loss of the wear area for all specimens. Statistical analysis was performed using Wilcoxon-Test regarding the two different time measurements for each material. For the analysis of the material variable, Kruskal-Wallis test was conducted followed by Mann-Whitney test. RESULTS: after 600 000 and 1 200 000 cycles, respectively. Artificial aging had significantly negative effect on the volume loss for all test materials. In addition, the choice of material had statistical influence on the outcome. CONCLUSION: Monolithic zirconia ceramic demonstrated lower wear than that reported for enamel after simulated 5-year of clinical service, whereas all other test materials showed higher volume loss after artificial aging.
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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 | 0.000 |
| 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".