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 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 | 0.001 |
| 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".