Failure analysis of high performance polymers and new generation cubic zirconia used for implant‐supported fixed, cantilevered prostheses
Bibliographic record
Abstract
BACKGROUND: The load-to-failure performance of computer-assisted design and computer-assisted manufacturing (CAD-CAM) high performance polymers (HPP) and new generation cubic zirconia (Zir) material when used with titanium (Ti) bases for implant-supported fixed prostheses with cantilevers is unknown. PURPOSE: To evaluate the load-to-failure performance of different CAD-CAM fabricated HPP and a new generation cubic Zir in a cantilevered situation when used with Ti bases. MATERIALS AND METHODS: Five specimens with a Ti base and five specimens without Ti bases were fabricated from seven different CAD-CAM HPPs (100% PEEK [J and CP], 80% PEEK with 20% filler [BRE], 80% PEKK with 20% filler [PK], ceramic reinforced PEEK [ZZ], interlaced fiberglass and resin [TR], fiber-composite material [T]). Five specimens with Ti base and two specimens without Ti base were prepared from a new generation cubic Zir (DD) and a 3Y-TZP Zir (FZR) as the control group (N = 84). All specimens (8 × 7 × 30 mm) were stabilized using a clamp attached to the first 20 mm of each specimen for a 10 mm cantilever. Static loading was applied vertically on the cantilever and the maximum load-to-failure values (N) were analyzed using a two-way ANOVA and t-test (alpha = .05). RESULTS: HPP and Zir specimens without Ti bases had significantly higher load-to-failure values than Ti based ones in all groups (P < .05). PK with Ti base had significantly lower load-to-failure values than other materials (P < .001). FZR showed significantly higher load-to-failure values than all HPPs and DD (P < .001). CONCLUSIONS: Load-to-failure values of HPPs and Zir were lower when Ti bases were used. New generation cubic Zir and all HPPs had lower load-to-failure values than FZR. HPPs performance varied among tested materials. PEKK with Ti base had the lowest load-to-failure value.
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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".