Fracture resistance of cantilevered full-arch implant-supported hybrid prostheses with carbon fiber frameworks after thermal cycling
Bibliographic record
Abstract
OBJECTIVES: This in vitro study aimed to find the best combination of mesostructure and veneering materials for full-arch implant-supported hybrid prostheses (HPs) in terms of the fracture resistance (FR) of their cantilevers. METHODS: Three groups (n = 5 each) of maxillary HPs were fabricated: Group-1 (CC-A, control): Co-Cr frameworks coated with acrylic resin; Group-2 (CF-A): carbon fiber veneered with acrylic resin; and Group-3 (CF-R): carbon fiber coated with composite resin. All specimens were submitted to 5,000 thermal cycles (5 °C - 55 °C, dwell time: 30 s), and subjected to a single cantilever bending test in a universal testing machine (crosshead speed: 0.5 mm/min) until failure. The fracture pattern was assessed using stereo microscope and SEM. The one-way ANOVA and Bonferroni tests were run (α= 0.05). RESULTS: The FR yielded significant differences among the three groups (p< 0.001). CC-A samples reached the highest FR values (p ≤ 0.001), whereas both CF-A and CF-R HPs exhibited the comparably (p = 0.107) lowest FR. CC-A specimens failed cohesively (100%): mostly without chipping (80%). CF-A mesostructures were always broken at the connections of the distal implants. CF-R prostheses often failed adhesively (80%). CONCLUSIONS: The HPs made of Co-Cr veneered with acrylic demonstrated the best mechanical behavior, being the only group whose 13-mm long cantilevers exceeded the clinically acceptable FR of 900 N. The HPs constructed with carbon fiber frameworks showed, additionally, more unfavorable fracture patterns. CLINICAL SIGNIFICANCE: For HPs with cantilevers up to 13 mm, Co-Cr mesostructures coated with acrylic may represent the optimum combination of materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".