Influence of novel implant selective laser melting framework design on mechanical durability of acrylic veneer
Bibliographic record
Abstract
BACKGROUND: A novel implant framework design is proposed to improve the mechanical durability of acrylic veneer. PURPOSES: Comparing the mechanical durability of acrylic veneer on implant frameworks fabricated from selective laser melting (SLM) with novel design against conventional computer numeric controlled (CNC) milled frameworks. MATERIALS AND METHODS: Implant titanium frameworks with distal cantilever were fabricated by SLM (n = 10) and CNC milling (n = 10). The CNC frameworks had multiple vertical pins, while the SLM frameworks had 3D metal networks of horizontal beams connected by vertical struts. All the frameworks were veneered with acrylic teeth and resin material and were subjected to a static load-to-failure test at the cantilever region. The load-to-failure readings and the pattern of prosthesis damage were recorded for each prosthesis. RESULTS: The CNC and SLM prostheses failed at statistically similar loads. The acrylic veneer around the CNC frameworks tend to initially crack around the distal implant followed by acrylic chipping. Six SLM prostheses failed at the framework connector on the mesial implant by separation of the screw seat. After reloading these prostheses, they failed by acrylic veneer chipping. The SLM prostheses had significantly less incidence of acrylic flexure and severity of acrylic veneer chipping than CNC prostheses. CONCLUSIONS: The SLM framework with novel design is efficient in reinforcing acrylic veneering. However, the SLM frameworks appeared weak in thin sections, such as the screw seat.
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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".