Effect of the interface and alloy composition on the in vitro marginal fit and mechanical properties of implant supported frameworks
Bibliographic record
Abstract
A precise or so-called passive fit between the implant supported prosthesis (ISP) and implant abutments is regarded essential to protect the load bearing capacity of the screw joints and implants from high stresses. The efficacy of section-and-solder and Preci-Disc methods to correct ISPs inaccuracies following their castings were evaluated as well as the load bearing capacity of the modified frameworks. Standardised ISP frameworks were fabricated using either Preci-Disc system (palladium-gold (PdAu); cobalt-chromium (CoCr) or silver-palladium-gold (Ag-Pd-Au) alloys) or cast-over method (PdAu alloy). The relative distortion of the frameworks to the prosthetic analogue was assessed before and after modification of the frameworks according to the specific modification method, using coordinate measuring machine. The load under which the modified frameworks failed was measured using universal testing apparatus. None of the measured frameworks provided a completely passive fit. However, their modification, by either section-and-solder or Preci-Disc system, reduced the three-dimensional distortion relative to the prosthetic analogue. The Preci-Disc system was the more efficacious system in improving framework fit to the master model and the resultant distortion values were lower than the components manufacturing tolerance. Regardless of the alloy used in this study, this system consistently produced passively fitted frameworks, without apparent reduction in their mechanical properties. Moreover, the latter system can be used intraorally and can reduce the cumulative error from all fabrication stages.
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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 | 0.001 | 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".